add i2v pipeline

scripts/convert_hunyuan_video1_5_to_diffusers.py

@@ -1,3 +1,4 @@
+# to convert only transformer
 """
 python scripts/convert_hunyuan_video1_5_to_diffusers.py \
     --original_state_dict_repo_id tencent/HunyuanVideo-1.5\
@@ -5,6 +6,7 @@ python scripts/convert_hunyuan_video1_5_to_diffusers.py \
     --transformer_type 480p_t2v
 """
 
+# to convert full pipeline
 """
 python scripts/convert_hunyuan_video1_5_to_diffusers.py \
     --original_state_dict_repo_id tencent/HunyuanVideo-1.5\
@@ -23,8 +25,8 @@ from safetensors.torch import load_file
 from huggingface_hub import snapshot_download, hf_hub_download
 
 import pathlib  
-from diffusers import HunyuanVideo15Transformer3DModel, AutoencoderKLHunyuanVideo15, FlowMatchEulerDiscreteScheduler, ClassifierFreeGuidance, HunyuanVideo15Pipeline
-from transformers import AutoModel, AutoTokenizer, T5EncoderModel, ByT5Tokenizer
+from diffusers import HunyuanVideo15Transformer3DModel, AutoencoderKLHunyuanVideo15, FlowMatchEulerDiscreteScheduler, ClassifierFreeGuidance, HunyuanVideo15Pipeline, HunyuanVideo15Image2VideoPipeline, HunyuanVideo15Text2VideoPipeline
+from transformers import AutoModel, AutoTokenizer, T5EncoderModel, ByT5Tokenizer, SiglipVisionModel, SiglipImageProcessor
 
 import json
 import argparse
@@ -812,6 +814,16 @@ def load_byt5(args):
     return encoder, tokenizer
 
 
+def load_siglip():
+    image_encoder = SiglipVisionModel.from_pretrained(
+        "black-forest-labs/FLUX.1-Redux-dev", subfolder="image_encoder", torch_dtype=torch.bfloat16
+        )
+    feature_extractor = SiglipImageProcessor.from_pretrained(
+        "black-forest-labs/FLUX.1-Redux-dev", subfolder="feature_extractor"
+    )
+    return image_encoder, feature_extractor
+
+
 def get_args():
     parser = argparse.ArgumentParser()
     parser.add_argument(
@@ -852,8 +864,9 @@ if __name__ == "__main__":
     if not args.save_pipeline:
         transformer.save_pretrained(args.output_path, safe_serialization=True)
     else:
-        vae = convert_vae(args)
+        task_type = transformer.config.task_type
 
+        vae = convert_vae(args)
 
         text_encoder, tokenizer = load_mllm()
         text_encoder_2, tokenizer_2 = load_byt5(args)
@@ -864,17 +877,35 @@ if __name__ == "__main__":
         guidance_scale = GUIDANCE_CONFIGS[args.transformer_type]["guidance_scale"]
         guider = ClassifierFreeGuidance(guidance_scale=guidance_scale)
 
-        pipeline = HunyuanVideo15Pipeline(
-            vae=vae,
-            text_encoder=text_encoder,
-            text_encoder_2=text_encoder_2,
-            tokenizer=tokenizer,
-            tokenizer_2=tokenizer_2,
-            transformer=transformer,
-            guider=guider,
-            scheduler=scheduler,
-        )
-        pipeline.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB")
+        if task_type == "i2v":
+            image_encoder, feature_extractor = load_siglip()
+            pipeline = HunyuanVideo15Image2VideoPipeline(
+                vae=vae,
+                text_encoder=text_encoder,
+                text_encoder_2=text_encoder_2,
+                tokenizer=tokenizer,
+                tokenizer_2=tokenizer_2,
+                transformer=transformer,
+                guider=guider,
+                scheduler=scheduler,
+                image_encoder=image_encoder,
+                feature_extractor=feature_extractor,
+            )
+        elif task_type == "t2v":
+            pipeline = HunyuanVideo15Text2VideoPipeline(
+                vae=vae,
+                text_encoder=text_encoder,
+                text_encoder_2=text_encoder_2,
+                tokenizer=tokenizer,
+                tokenizer_2=tokenizer_2,
+                transformer=transformer,
+                guider=guider,
+                scheduler=scheduler,
+            )
+        else:
+            raise ValueError(f"Task type {task_type} is not supported")
+
+        pipeline.save_pretrained(args.output_path, safe_serialization=True)
 
 
 

src/diffusers/__init__.py

@@ -483,6 +483,7 @@ else:
             "HunyuanVideoImageToVideoPipeline",
             "HunyuanVideoPipeline",
             "HunyuanVideo15Pipeline",
+            "HunyuanVideo15ImageToVideoPipeline",
             "I2VGenXLPipeline",
             "IFImg2ImgPipeline",
             "IFImg2ImgSuperResolutionPipeline",
@@ -1170,6 +1171,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             HunyuanVideoImageToVideoPipeline,
             HunyuanVideoPipeline,
             HunyuanVideo15Pipeline,
+            HunyuanVideo15ImageToVideoPipeline,
             I2VGenXLPipeline,
             IFImg2ImgPipeline,
             IFImg2ImgSuperResolutionPipeline,

src/diffusers/pipelines/__init__.py

@@ -242,7 +242,7 @@ else:
         "HunyuanVideoImageToVideoPipeline",
         "HunyuanVideoFramepackPipeline",
     ]
-    _import_structure["hunyuan_video1_5"] = ["HunyuanVideo15Pipeline"]
+    _import_structure["hunyuan_video1_5"] = ["HunyuanVideo15Pipeline", "HunyuanVideo15ImageToVideoPipeline"]
     _import_structure["hunyuan_image"] = ["HunyuanImagePipeline", "HunyuanImageRefinerPipeline"]
     _import_structure["kandinsky"] = [
         "KandinskyCombinedPipeline",
@@ -663,7 +663,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             HunyuanVideoImageToVideoPipeline,
             HunyuanVideoPipeline,
         )
-        from .hunyuan_video1_5 import HunyuanVideo15Pipeline
+        from .hunyuan_video1_5 import HunyuanVideo15Pipeline, HunyuanVideo15ImageToVideoPipeline
         from .hunyuandit import HunyuanDiTPipeline
         from .i2vgen_xl import I2VGenXLPipeline
         from .kandinsky import (

src/diffusers/pipelines/hunyuan_video1_5/__init__.py

@@ -23,6 +23,7 @@ except OptionalDependencyNotAvailable:
     _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
 else:
     _import_structure["pipeline_hunyuan_video1_5"] = ["HunyuanVideo15Pipeline"]
+    _import_structure["pipeline_hunyuan_video1_5_image2video"] = ["HunyuanVideo15ImageToVideoPipeline"]
 
 if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     try:
@@ -33,6 +34,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from ...utils.dummy_torch_and_transformers_objects import *
     else:
         from .pipeline_hunyuan_video1_5 import HunyuanVideo15Pipeline
+        from .pipeline_hunyuan_video1_5_image2video import HunyuanVideo15ImageToVideoPipeline
 
 else:
     import sys

src/diffusers/pipelines/hunyuan_video1_5/image_processor.py

@@ -80,11 +80,13 @@ class HunyuanVideo15ImageProcessor(VideoProcessor):
         do_resize: bool = True,
         vae_scale_factor: int = 16,
         vae_latent_channels: int = 32,
+        do_convert_rgb: bool = True,
     ):
         super().__init__(
             do_resize=do_resize,
             vae_scale_factor=vae_scale_factor,
-            vae_latent_channels=vae_latent_channels
+            vae_latent_channels=vae_latent_channels,
+            do_convert_rgb=do_convert_rgb,
         )
 
 

src/diffusers/pipelines/hunyuan_video1_5/pipeline_hunyuan_video1_5.py

@@ -759,26 +759,20 @@ class HunyuanVideo15Pipeline(DiffusionPipeline):
             height,
             width,
             num_frames,
-            torch.float32,
+            self.transformer.dtype,
             device,
             generator,
             latents,
         )
-        cond_latents_concat, mask_concat = self.prepare_cond_latents_and_mask(latents, torch.float32, device)
+        cond_latents_concat, mask_concat = self.prepare_cond_latents_and_mask(latents, self.transformer.dtype, device)
         image_embeds = torch.zeros(
             batch_size, 
             self.vision_num_semantic_tokens, 
             self.vision_states_dim,
-            dtype=torch.float32,
+            dtype=self.transformer.dtype,
             device=device
         )
 
-        image_embeds = image_embeds.to(self.transformer.dtype)
-        latents=latents.to(self.transformer.dtype)
-        cond_latents_concat=cond_latents_concat.to(self.transformer.dtype)
-        mask_concat=mask_concat.to(self.transformer.dtype)
-
-
         # 7. Denoising loop
         num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
         self._num_timesteps = len(timesteps)

src/diffusers/pipelines/hunyuan_video1_5/pipeline_hunyuan_video1_5_image2video.py

@@ -0,0 +1,975 @@
+# Copyright 2025 The HunyuanVideo 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, Tuple, Union
+import re
+
+import numpy as np
+import torch
+from transformers import Qwen2_5_VLTextModel, Qwen2Tokenizer, T5EncoderModel, ByT5Tokenizer, SiglipVisionModel, SiglipImageProcessor
+
+from ...models import AutoencoderKLHunyuanVideo15, HunyuanVideo15Transformer3DModel
+from ...schedulers import FlowMatchEulerDiscreteScheduler
+from ...utils import is_torch_xla_available, logging, replace_example_docstring
+from .image_processor import HunyuanVideo15ImageProcessor
+from ..pipeline_utils import DiffusionPipeline
+from .pipeline_output import HunyuanVideo15PipelineOutput
+from ...guiders import ClassifierFreeGuidance
+from ...utils.torch_utils import randn_tensor
+
+
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```python
+        >>> import torch
+        >>> from diffusers import HunyuanVideo15Pipeline
+        >>> from diffusers.utils import export_to_video
+
+        >>> model_id = "hunyuanvideo-community/HunyuanVideo15"
+        >>> pipe = HunyuanVideo15Pipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+        >>> pipe.vae.enable_tiling()
+        >>> pipe.to("cuda")
+
+        >>> output = pipe(
+        ...     prompt="A cat walks on the grass, realistic",
+        ...     num_inference_steps=50,
+        ... ).frames[0]
+        >>> export_to_video(output, "output.mp4", fps=15)
+        ```
+"""
+
+
+def format_text_input(prompt: List[str], system_message: str
+        ) -> List[Dict[str, Any]]:
+    """
+    Apply text to template.
+
+    Args:
+        prompt (List[str]): Input text.
+        system_message (str): System message.
+
+    Returns:
+        List[Dict[str, Any]]: List of chat conversation.
+    """
+
+    template = [
+        [
+            {
+                'role': 'system', 
+                'content': system_message}, 
+            {'role': 'user', 'content': p if p else " "}
+        ]
+        for p in prompt]
+
+    return template
+
+
+def extract_glyph_texts(prompt: str) -> List[str]:
+    """
+    Extract glyph texts from prompt using regex pattern.
+    
+    Args:
+        prompt: Input prompt string
+        
+    Returns:
+        List of extracted glyph texts
+    """
+    pattern = r'\"(.*?)\"|“(.*?)”'
+    matches = re.findall(pattern, prompt)
+    result = [match[0] or match[1] for match in matches]
+    result = list(dict.fromkeys(result)) if len(result) > 1 else result
+
+    if result:
+        formatted_result = ". ".join([f'Text "{text}"' for text in result]) + ". "
+    else:
+        formatted_result = None
+
+    return formatted_result
+
+
+# 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 HunyuanVideo15Image2VideoPipeline(DiffusionPipeline):
+    r"""
+    Pipeline for image-to-video generation using HunyuanVideo1.5.
+
+    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:
+        transformer ([`HunyuanVideo15Transformer3DModel`]):
+            Conditional Transformer (MMDiT) architecture to denoise the encoded video latents.
+        scheduler ([`FlowMatchEulerDiscreteScheduler`]):
+            A scheduler to be used in combination with `transformer` to denoise the encoded video latents.
+        vae ([`AutoencoderKLHunyuanVideo15`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
+        text_encoder ([`Qwen2.5-VL-7B-Instruct`]):
+            [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct), specifically the
+            [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) variant.
+        tokenizer (`Qwen2Tokenizer`): Tokenizer of class [Qwen2Tokenizer].
+        text_encoder_2 ([`T5EncoderModel`]):
+            [T5EncoderModel](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel)
+            variant.
+        tokenizer_2 (`ByT5Tokenizer`): Tokenizer of class [ByT5Tokenizer]
+        guider ([`ClassifierFreeGuidance`]):
+            [ClassifierFreeGuidance]for classifier free guidance.
+    """
+
+    model_cpu_offload_seq = "text_encoder->text_encoder_2->transformer->vae"
+    _callback_tensor_inputs = ["latents", "prompt_embeds"]
+
+    def __init__(
+        self,
+        text_encoder: Qwen2_5_VLTextModel,
+        tokenizer: Qwen2Tokenizer,
+        transformer: HunyuanVideo15Transformer3DModel,
+        vae: AutoencoderKLHunyuanVideo15,
+        scheduler: FlowMatchEulerDiscreteScheduler,
+        text_encoder_2: T5EncoderModel,
+        tokenizer_2: ByT5Tokenizer,
+        guider: ClassifierFreeGuidance,
+        image_encoder: SiglipVisionModel,
+        feature_extractor: SiglipImageProcessor,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            transformer=transformer,
+            scheduler=scheduler,
+            text_encoder_2=text_encoder_2,
+            tokenizer_2=tokenizer_2,
+            guider=guider,
+            image_encoder=image_encoder,
+            feature_extractor=feature_extractor,
+        )
+
+        self.vae_scale_factor_temporal = self.vae.temporal_compression_ratio if getattr(self, "vae", None) else 4
+        self.vae_scale_factor_spatial = self.vae.spatial_compression_ratio if getattr(self, "vae", None) else 16
+        self.video_processor = HunyuanVideo15ImageProcessor(vae_scale_factor=self.vae_scale_factor_spatial, do_resize=False, do_convert_rgb=True)
+        self.target_size = self.transformer.config.target_size if getattr(self, "transformer", None) else 640
+        self.vision_states_dim = self.transformer.config.image_embed_dim if getattr(self, "transformer", None) else 1152
+        self.num_channels_latents = self.vae.config.latent_channels if hasattr(self, "vae") else 32
+        # fmt: off
+        self.system_message = "You are a helpful assistant. Describe the video by detailing the following aspects: \
+        1. The main content and theme of the video. \
+        2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects. \
+        3. Actions, events, behaviors temporal relationships, physical movement changes of the objects. \
+        4. background environment, light, style and atmosphere. \
+        5. camera angles, movements, and transitions used in the video."
+        # fmt: on
+        self.prompt_template_encode_start_idx = 108
+        self.tokenizer_max_length = 1000
+        self.tokenizer_2_max_length = 256
+        self.vision_num_semantic_tokens = 729
+
+
+    @staticmethod
+    # Copied from diffusers.pipelines.hunyuan_video1_5.pipeline_hunyuan_video1_5.HunyuanVideo15Pipeline._get_mllm_prompt_embeds
+    def _get_mllm_prompt_embeds(
+        text_encoder: Qwen2_5_VLTextModel,
+        tokenizer: Qwen2Tokenizer,
+        prompt: Union[str, List[str]],
+        device: Optional[torch.device] = None,
+        tokenizer_max_length: int = 1000,
+        num_hidden_layers_to_skip: int = 2,
+        # fmt: off
+        system_message: str = "You are a helpful assistant. Describe the video by detailing the following aspects: \
+        1. The main content and theme of the video. \
+        2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects. \
+        3. Actions, events, behaviors temporal relationships, physical movement changes of the objects. \
+        4. background environment, light, style and atmosphere. \
+        5. camera angles, movements, and transitions used in the video.",
+        # fmt: on
+        crop_start: int = 108,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+
+        prompt = format_text_input(prompt, system_message)
+
+        text_inputs = tokenizer.apply_chat_template(
+            prompt,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+            padding="max_length",
+            max_length=tokenizer_max_length + crop_start,
+            truncation=True,
+            return_tensors="pt",
+        )
+
+        text_input_ids = text_inputs.input_ids.to(device=device)
+        prompt_attention_mask = text_inputs.attention_mask.to(device=device)
+
+        prompt_embeds = text_encoder(
+            input_ids=text_input_ids,
+            attention_mask=prompt_attention_mask,
+            output_hidden_states=True,
+        ).hidden_states[-(num_hidden_layers_to_skip + 1)]
+
+        if crop_start is not None and crop_start > 0:
+            prompt_embeds = prompt_embeds[:, crop_start:]
+            prompt_attention_mask = prompt_attention_mask[:, crop_start:]
+
+
+        return prompt_embeds, prompt_attention_mask
+
+    
+    @staticmethod
+    # Copied from diffusers.pipelines.hunyuan_video1_5.pipeline_hunyuan_video1_5.HunyuanVideo15Pipeline._get_byt5_prompt_embeds
+    def _get_byt5_prompt_embeds(
+        tokenizer: ByT5Tokenizer,
+        text_encoder: T5EncoderModel,
+        prompt: Union[str, List[str]],
+        device: Optional[torch.device] = None,
+        tokenizer_max_length: int = 256,
+    ):
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+
+        glyph_texts = [extract_glyph_texts(p) for p in prompt]
+
+        prompt_embeds_list = []
+        prompt_embeds_mask_list = []
+
+        for glyph_text in glyph_texts:
+            if glyph_text is None:
+                glyph_text_embeds = torch.zeros(
+                    (1, tokenizer_max_length, text_encoder.config.d_model), device=device, text_encoder.dtype
+                )
+                glyph_text_embeds_mask = torch.zeros(
+                    (1, tokenizer_max_length), device=device, dtype=torch.int64
+                )
+            else:
+                txt_tokens = tokenizer(
+                    glyph_text,
+                    padding="max_length",
+                    max_length=tokenizer_max_length,
+                    truncation=True,
+                    add_special_tokens=True,
+                    return_tensors="pt",
+                ).to(device)
+
+                glyph_text_embeds = text_encoder(
+                    input_ids=txt_tokens.input_ids,
+                    attention_mask=txt_tokens.attention_mask.float(),
+                )[0]
+                glyph_text_embeds = glyph_text_embeds.to(device=device)
+                glyph_text_embeds_mask = txt_tokens.attention_mask.to(device=device)
+
+            prompt_embeds_list.append(glyph_text_embeds)
+            prompt_embeds_mask_list.append(glyph_text_embeds_mask)
+
+        prompt_embeds = torch.cat(prompt_embeds_list, dim=0)
+        prompt_embeds_mask = torch.cat(prompt_embeds_mask_list, dim=0)
+
+        return prompt_embeds, prompt_embeds_mask
+
+
+    @staticmethod
+    def _get_vae_image_latents(
+        vae: AutoencoderKLHunyuanVideo15, 
+        image_processor: HunyuanVideo15ImageProcessor,
+        image: PIL.Image.Image,
+        height: int,
+        width: int,
+        device: torch.device,
+    ) -> torch.Tensor:
+        
+        vae_dtype = self.vae.dtype
+        image_tensor = image_processor.preprocess(image, height=height, width=width).to(device, dtype=vae_dtype)
+        image_latents = retrieve_latents(vae.encode(image_tensor), sample_mode="argmax")
+        image_latents = image_latents * vae.config.scaling_factor
+        return image_latents
+
+    
+    @staticmethod
+    def _get_image_embeds(
+        image_encoder: SiglipVisionModel,
+        feature_extractor: SiglipImageProcessor,
+        image: PIL.Image.Image,
+        device: torch.device,
+    ) -> torch.Tensor:
+        
+        image_encoder_dtype = next(image_encoder.parameters()).dtype
+        image = feature_extractor.preprocess(
+            images=image, do_resize=True, return_tensors="pt", do_convert_rgb=True
+        )
+        image = image.to(device=device, dtype=image_encoder_dtype)
+        image_enc_hidden_states = image_encoder(**image).last_hidden_state
+
+        return image_enc_hidden_states
+
+    def encode_image(
+        self,
+        image: PIL.Image.Image,
+        batch_size: int,
+        device: torch.device,
+        dtype: torch.dtype,
+    ) -> torch.Tensor:
+        
+        image_embeds = self._get_image_embeds(
+            image_encoder=self.image_encoder,
+            feature_extractor=self.feature_extractor,
+            image=image,
+            device=device,
+        )
+        image_embeds = image_embeds.repeat(batch_size, 1, 1)
+        image_embeds = image_embeds.to(device=device, dtype=dtype)
+        return image_embeds
+    
+    # Copied from diffusers.pipelines.hunyuan_video1_5.pipeline_hunyuan_video1_5.HunyuanVideo15Pipeline.encode_prompt
+    def encode_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+        batch_size: int = 1,
+        num_videos_per_prompt: int = 1,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        prompt_embeds_mask: Optional[torch.Tensor] = None,
+        prompt_embeds_2: Optional[torch.Tensor] = None,
+        prompt_embeds_mask_2: Optional[torch.Tensor] = None,
+    ):
+        r"""
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            device: (`torch.device`):
+                torch device
+            batch_size (`int`):
+                batch size of prompts, defaults to 1
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. If not provided, text embeddings will be generated from `prompt` input
+                argument.
+            prompt_embeds_mask (`torch.Tensor`, *optional*):
+                Pre-generated text mask. If not provided, text mask will be generated from `prompt` input argument.
+            prompt_embeds_2 (`torch.Tensor`, *optional*):
+                Pre-generated glyph text embeddings from ByT5. If not provided, will be generated from `prompt` input
+                argument using self.tokenizer_2 and self.text_encoder_2.
+            prompt_embeds_mask_2 (`torch.Tensor`, *optional*):
+                Pre-generated glyph text mask from ByT5. If not provided, will be generated from `prompt` input
+                argument using self.tokenizer_2 and self.text_encoder_2.
+        """
+        device = device or self._execution_device
+        dtype = dtype or self.text_encoder.dtype
+
+        if prompt is None:
+            prompt = [""] * batch_size
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+
+        if prompt_embeds is None:
+            prompt_embeds, prompt_embeds_mask = self._get_mllm_prompt_embeds(
+                tokenizer=self.tokenizer,
+                text_encoder=self.text_encoder,
+                prompt=prompt,
+                device=device,
+                tokenizer_max_length=self.tokenizer_max_length,
+                system_message=self.system_message,
+                crop_start=self.prompt_template_encode_start_idx,
+            )
+
+        if prompt_embeds_2 is None:
+            prompt_embeds_2, prompt_embeds_mask_2 = self._get_byt5_prompt_embeds(
+                tokenizer=self.tokenizer_2,
+                text_encoder=self.text_encoder_2,
+                prompt=prompt,
+                device=device,
+                tokenizer_max_length=self.tokenizer_2_max_length,
+            )
+
+        _, 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)
+        prompt_embeds_mask = prompt_embeds_mask.repeat(1, num_videos_per_prompt, 1)
+        prompt_embeds_mask = prompt_embeds_mask.view(batch_size * num_videos_per_prompt, seq_len)
+
+        _, seq_len_2, _ = prompt_embeds_2.shape
+        prompt_embeds_2 = prompt_embeds_2.repeat(1, num_videos_per_prompt, 1)
+        prompt_embeds_2 = prompt_embeds_2.view(batch_size * num_videos_per_prompt, seq_len_2, -1)
+        prompt_embeds_mask_2 = prompt_embeds_mask_2.repeat(1, num_videos_per_prompt, 1)
+        prompt_embeds_mask_2 = prompt_embeds_mask_2.view(batch_size * num_videos_per_prompt, seq_len_2)
+
+        prompt_embeds = prompt_embeds.to(device=device, dtype=dtype)
+        prompt_embeds_mask = prompt_embeds_mask.to(device=device, dtype=dtype)
+        prompt_embeds_2 = prompt_embeds_2.to(device=device, dtype=dtype)
+        prompt_embeds_mask_2 = prompt_embeds_mask_2.to(device=device, dtype=dtype)
+
+        return prompt_embeds, prompt_embeds_mask, prompt_embeds_2, prompt_embeds_mask_2
+
+    def check_inputs(
+        self,
+        prompt,
+        image: PIL.Image.Image,
+        negative_prompt=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        prompt_embeds_mask=None,
+        negative_prompt_embeds_mask=None,
+        prompt_embeds_2=None,
+        prompt_embeds_mask_2=None,
+        negative_prompt_embeds_2=None,
+        negative_prompt_embeds_mask_2=None,
+    ):  
+        if not isinstance(image, PIL.Image.Image):
+            raise ValueError(f"`image` has to be of type `PIL.Image.Image` but is {type(image)}")
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and prompt_embeds_mask is None:
+            raise ValueError(
+                "If `prompt_embeds` are provided, `prompt_embeds_mask` also have to be passed. Make sure to generate `prompt_embeds_mask` from the same text encoder that was used to generate `prompt_embeds`."
+            )
+        if negative_prompt_embeds is not None and negative_prompt_embeds_mask is None:
+            raise ValueError(
+                "If `negative_prompt_embeds` are provided, `negative_prompt_embeds_mask` also have to be passed. Make sure to generate `negative_prompt_embeds_mask` from the same text encoder that was used to generate `negative_prompt_embeds`."
+            )
+
+        if prompt is None and prompt_embeds_2 is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds_2`. Cannot leave both `prompt` and `prompt_embeds_2` undefined."
+            )
+
+        if prompt_embeds_2 is not None and prompt_embeds_mask_2 is None:
+            raise ValueError(
+                "If `prompt_embeds_2` are provided, `prompt_embeds_mask_2` also have to be passed. Make sure to generate `prompt_embeds_mask_2` from the same text encoder that was used to generate `prompt_embeds_2`."
+            )
+        if negative_prompt_embeds_2 is not None and negative_prompt_embeds_mask_2 is None:
+            raise ValueError(
+                "If `negative_prompt_embeds_2` are provided, `negative_prompt_embeds_mask_2` also have to be passed. Make sure to generate `negative_prompt_embeds_mask_2` from the same text encoder that was used to generate `negative_prompt_embeds_2`."
+            )
+
+    # Copied from diffusers.pipelines.hunyuan_video1_5.pipeline_hunyuan_video1_5.HunyuanVideo15Pipeline.prepare_latents
+    def prepare_latents(
+        self,
+        batch_size: int,
+        num_channels_latents: int = 32,
+        height: int = 720,
+        width: int = 1280,
+        num_frames: int = 129,
+        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 latents is not None:
+            return latents.to(device=device, dtype=dtype)
+
+        shape = (
+            batch_size,
+            num_channels_latents,
+            (num_frames - 1) // self.vae_scale_factor_temporal + 1,
+            int(height) // self.vae_scale_factor_spatial,
+            int(width) // self.vae_scale_factor_spatial,
+        )
+        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."
+            )
+
+        latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        return latents
+
+
+    def prepare_cond_latents_and_mask(
+        self, 
+        latents: torch.Tensor, 
+        image: PIL.Image.Image,
+        batch_size: int,
+        height: int,
+        width: int,
+        dtype: torch.dtype, 
+        device: torch.device,
+    ):
+        """
+        Prepare conditional latents and mask for t2v generation.
+        
+        Args:
+            latents: Main latents tensor (B, C, F, H, W)
+            
+        Returns:
+            tuple: (cond_latents_concat, mask_concat) - both are zero tensors for t2v
+        """
+
+        batch, channels, frames, height, width = latents.shape
+
+        image_latents = self._get_vae_image_latents(
+            vae=self.vae,
+            image_processor=self.video_processor,
+            image=image,
+            height=height,
+            width=width,
+            device=device,
+        )
+        
+        latent_condition = image_latents.repeat(batch_size, 1, frames, 1, 1)
+        latent_condition[:,:,1:, :, :] = 0
+        latent_condition = latent_condition.to(device=device, dtype=dtype)
+        
+        latent_mask = torch.zeros(
+            batch, 1, frames, height, width,
+            dtype=dtype,
+            device=device
+        )
+        latent_mask[:,:, 0, :, :] = 1.0
+        
+        return latent_condition, latent_mask
+
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def attention_kwargs(self):
+        return self._attention_kwargs
+
+    @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: PIL.Image.Image,
+        prompt: Union[str, List[str]] = None,
+        negative_prompt: Union[str, List[str]] = None,
+        num_frames: int = 121,
+        num_inference_steps: int = 50,
+        sigmas: List[float] = None,
+        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,
+        prompt_embeds_mask: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds_mask: Optional[torch.Tensor] = None,
+        prompt_embeds_2: Optional[torch.Tensor] = None,
+        prompt_embeds_mask_2: Optional[torch.Tensor] = None,
+        negative_prompt_embeds_2: Optional[torch.Tensor] = None,
+        negative_prompt_embeds_mask_2: Optional[torch.Tensor] = None,
+        output_type: Optional[str] = "np",
+        return_dict: bool = True,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+    ):
+        r"""
+        The call function to the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+                will be used instead.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `true_cfg_scale` is
+                not greater than `1`).
+            negative_prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
+                `text_encoder_2`. If not defined, `negative_prompt` is used in all the text-encoders.
+            height (`int`, defaults to `720`):
+                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 `129`):
+                The number of frames in the generated video.
+            num_inference_steps (`int`, 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.
+            true_cfg_scale (`float`, *optional*, defaults to 1.0):
+                True classifier-free guidance (guidance scale) is enabled when `true_cfg_scale` > 1 and
+                `negative_prompt` is provided.
+            guidance_scale (`float`, defaults to `6.0`):
+                Embedded guiddance scale is enabled by setting `guidance_scale` > 1. Higher `guidance_scale` encourages
+                a model to generate images more aligned with `prompt` at the expense of lower image quality.
+
+                Guidance-distilled models approximates true classifer-free guidance for `guidance_scale` > 1. Refer to
+                the [paper](https://huggingface.co/papers/2210.03142) to learn more.
+            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 (prompt weighting). If not
+                provided, text embeddings are generated from the `prompt` input argument.
+            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+                If not provided, pooled text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`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.
+            negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, pooled 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 [`HunyuanVideoPipelineOutput`] instead of a plain tuple.
+            attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            clip_skip (`int`, *optional*):
+                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
+                the output of the pre-final layer will be used for computing the prompt embeddings.
+            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.
+
+        Examples:
+
+        Returns:
+            [`~HunyuanVideoPipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`HunyuanVideoPipelineOutput`] 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.
+        """
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt=prompt,
+            image=image,
+            negative_prompt=negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            prompt_embeds_mask=prompt_embeds_mask,
+            negative_prompt_embeds_mask=negative_prompt_embeds_mask,
+            prompt_embeds_2=prompt_embeds_2,
+            prompt_embeds_mask_2=prompt_embeds_mask_2,
+            negative_prompt_embeds_2=negative_prompt_embeds_2,
+            negative_prompt_embeds_mask_2=negative_prompt_embeds_mask_2,
+        )
+
+        
+        height, width = self.video_processor.calculate_default_height_width(height=image.size[1], width=image.size[0], target_size=self.target_size)
+        image = self.video_processor.resize(image, height=height, width=width, resize_mode="crop")
+
+        self._attention_kwargs = attention_kwargs
+        self._current_timestep = None
+        self._interrupt = False
+
+        device = self._execution_device
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        # 3. Encode input prompt
+        prompt_embeds, prompt_embeds_mask, prompt_embeds_2, prompt_embeds_mask_2 = self.encode_prompt(
+            prompt=prompt,
+            device=device,
+            dtype=self.transformer.dtype,
+            batch_size=batch_size,
+            num_videos_per_prompt=num_videos_per_prompt,
+            prompt_embeds=prompt_embeds,
+            prompt_embeds_mask=prompt_embeds_mask,
+            prompt_embeds_2=prompt_embeds_2,
+            prompt_embeds_mask_2=prompt_embeds_mask_2,
+        )
+
+        if self.guider._enabled and self.guider.num_conditions >1 :
+            negative_prompt_embeds, negative_prompt_embeds_mask, negative_prompt_embeds_2, negative_prompt_embeds_mask_2 = self.encode_prompt(
+                prompt=negative_prompt,
+                device=device,
+                dtype=self.transformer.dtype,
+                batch_size=batch_size,
+                num_videos_per_prompt=num_videos_per_prompt,
+                prompt_embeds=negative_prompt_embeds,
+                prompt_embeds_mask=negative_prompt_embeds_mask,
+                prompt_embeds_2=negative_prompt_embeds_2,
+                prompt_embeds_mask_2=negative_prompt_embeds_mask_2,
+            )
+
+        # 4. Prepare timesteps
+        sigmas = np.linspace(1.0, 0.0, num_inference_steps + 1)[:-1] if sigmas is None else sigmas
+        timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, sigmas=sigmas)
+
+        # 5. Prepare latent variables
+        latents = self.prepare_latents(
+            batch_size=batch_size * num_videos_per_prompt,
+            num_channels_latents=self.num_channels_latents,
+            height=height,
+            width=width,
+            num_frames=num_frames,
+            dtype=self.transformer.dtype,
+            device=device,
+            generator=generator,
+            latents=latents,
+        )
+       
+        cond_latents_concat, mask_concat = self.prepare_cond_latents_and_mask(
+            latents =latenets, 
+            image=image,
+            batch_size=batch_size * num_videos_per_prompt,
+            height=height,
+            width=width,
+            dtype=self.transformer.dtype, 
+            device=device
+        )
+        image_embeds = self.encode_image(
+            image=image,
+            batch_size=batch_size * num_videos_per_prompt,
+            device=device,
+            dtype=self.transformer.dtype,
+        )
+
+        # 7. Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        self._num_timesteps = len(timesteps)
+
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                self._current_timestep = t
+                latent_model_input = torch.cat([latents, cond_latents_concat, mask_concat], dim=1)
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latent_model_input.shape[0]).to(latent_model_input.dtype)
+
+                # Step 1: Collect model inputs needed for the guidance method
+                # conditional inputs should always be first element in the tuple
+                guider_inputs = {
+                    "encoder_hidden_states": (prompt_embeds, negative_prompt_embeds),
+                    "encoder_attention_mask": (prompt_embeds_mask, negative_prompt_embeds_mask),
+                    "encoder_hidden_states_2": (prompt_embeds_2, negative_prompt_embeds_2),
+                    "encoder_attention_mask_2": (prompt_embeds_mask_2, negative_prompt_embeds_mask_2),
+                }
+
+                # Step 2: Update guider's internal state for this denoising step
+                self.guider.set_state(step=i, num_inference_steps=num_inference_steps, timestep=t)
+
+                # Step 3: Prepare batched model inputs based on the guidance method
+                # The guider splits model inputs into separate batches for conditional/unconditional predictions.
+                # For CFG with guider_inputs = {"encoder_hidden_states": (prompt_embeds, negative_prompt_embeds)}:
+                # you will get a guider_state with two batches:
+                #   guider_state = [
+                #       {"encoder_hidden_states": prompt_embeds, "__guidance_identifier__": "pred_cond"},      # conditional batch
+                #       {"encoder_hidden_states": negative_prompt_embeds, "__guidance_identifier__": "pred_uncond"},  # unconditional batch
+                #   ]
+                # Other guidance methods may return 1 batch (no guidance) or 3+ batches (e.g., PAG, APG).
+                guider_state = self.guider.prepare_inputs(guider_inputs)
+                # Step 4: Run the denoiser for each batch
+                # Each batch in guider_state represents a different conditioning (conditional, unconditional, etc.).
+                # We run the model once per batch and store the noise prediction in guider_state_batch.noise_pred.
+                for guider_state_batch in guider_state:
+                    self.guider.prepare_models(self.transformer)
+
+                    # Extract conditioning kwargs for this batch (e.g., encoder_hidden_states)
+                    cond_kwargs = {
+                        input_name: getattr(guider_state_batch, input_name) for input_name in guider_inputs.keys()
+                    }
+
+                    # e.g. "pred_cond"/"pred_uncond"
+                    context_name = getattr(guider_state_batch, self.guider._identifier_key)
+                    with self.transformer.cache_context(context_name):
+                        # Run denoiser and store noise prediction in this batch
+                        guider_state_batch.noise_pred = self.transformer(
+                            hidden_states=latent_model_input,
+                            image_embeds=image_embeds,
+                            timestep=timestep,
+                            attention_kwargs=self.attention_kwargs,
+                            return_dict=False,
+                            **cond_kwargs,
+                        )[0]
+
+                    # Cleanup model (e.g., remove hooks)
+                    self.guider.cleanup_models(self.transformer)
+
+                # Step 5: Combine predictions using the guidance method
+                # The guider takes all noise predictions from guider_state and combines them according to the guidance algorithm.
+                # Continuing the CFG example, the guider receives:
+                #   guider_state = [
+                #       {"encoder_hidden_states": prompt_embeds, "noise_pred": noise_pred_cond, "__guidance_identifier__": "pred_cond"},      # batch 0
+                #       {"encoder_hidden_states": negative_prompt_embeds, "noise_pred": noise_pred_uncond, "__guidance_identifier__": "pred_uncond"},  # batch 1
+                #   ]
+                # And extracts predictions using the __guidance_identifier__:
+                #   pred_cond = guider_state[0]["noise_pred"]      # extracts noise_pred_cond
+                #   pred_uncond = guider_state[1]["noise_pred"]    # extracts noise_pred_uncond
+                # Then applies CFG formula:
+                #   noise_pred = pred_uncond + guidance_scale * (pred_cond - pred_uncond)
+                # Returns GuiderOutput(pred=noise_pred, pred_cond=pred_cond, pred_uncond=pred_uncond)
+                noise_pred = self.guider(guider_state)[0]
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                # 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 = latents.to(self.vae.dtype) / self.vae.config.scaling_factor
+            video = self.vae.decode(latents, return_dict=False)[0]
+            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 HunyuanVideo15PipelineOutput(frames=video)