diffusers/pr_14421/en/api/pipelines/hunyuan_video15.md

#
# 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. -->

# HunyuanVideo-1.5

HunyuanVideo-1.5 is a lightweight yet powerful video generation model that achieves state-of-the-art visual quality and motion coherence with only 8.3 billion parameters, enabling efficient inference on consumer-grade GPUs. This achievement is built upon several key components, including meticulous data curation, an advanced DiT architecture with selective and sliding tile attention (SSTA), enhanced bilingual understanding through glyph-aware text encoding, progressive pre-training and post-training, and an efficient video super-resolution network. Leveraging these designs, we developed a unified framework capable of high-quality text-to-video and image-to-video generation across multiple durations and resolutions. Extensive experiments demonstrate that this compact and proficient model establishes a new state-of-the-art among open-source models.

You can find all the original HunyuanVideo checkpoints under the [Tencent](https://huggingface.co/tencent) organization.

> [!TIP]
> Click on the HunyuanVideo models in the right sidebar for more examples of video generation tasks.
>
> The examples below use a checkpoint from [hunyuanvideo-community](https://huggingface.co/hunyuanvideo-community) because the weights are stored in a layout compatible with Diffusers.

The example below demonstrates how to generate a video optimized for memory or inference speed.

Refer to the [Reduce memory usage](../../optimization/memory) guide for more details about the various memory saving techniques.

```py
import torch
from diffusers import AutoModel, HunyuanVideo15Pipeline
from diffusers.utils import export_to_video

pipeline = HunyuanVideo15Pipeline.from_pretrained(
    "HunyuanVideo-1.5-Diffusers-480p_t2v",
    dtype=torch.bfloat16,
)

# model-offloading and tiling
pipeline.enable_model_cpu_offload()
pipeline.vae.enable_tiling()

prompt = "A fluffy teddy bear sits on a bed of soft pillows surrounded by children's toys."
video = pipeline(prompt=prompt, num_frames=61, num_inference_steps=30).frames[0]
export_to_video(video, "output.mp4", fps=15)
```

## Notes

- HunyuanVideo1.5 use attention masks with variable-length sequences. For best performance, we recommend using an attention backend that handles padding efficiently.

    - **H100/H800:** `_flash_3_hub` or `_flash_3_varlen_hub`
    - **A100/A800/RTX 4090:** `flash_hub` or `flash_varlen_hub`
    - **Other GPUs:** `sage_hub`

Refer to the [Attention backends](../../optimization/attention_backends) guide for more details about using a different backend.

```py
pipe.transformer.set_attention_backend("flash_hub")  # or your preferred backend
```

- [HunyuanVideo15Pipeline](/docs/diffusers/pr_14421/en/api/pipelines/hunyuan_video15#diffusers.HunyuanVideo15Pipeline) use guider and does not take `guidance_scale` parameter at runtime. 

You can check the default guider configuration using `pipe.guider`:

```py
>>> pipe.guider 
ClassifierFreeGuidance {
  "_class_name": "ClassifierFreeGuidance",
  "_diffusers_version": "0.36.0.dev0",
  "enabled": true,
  "guidance_rescale": 0.0,
  "guidance_scale": 6.0,
  "start": 0.0,
  "stop": 1.0,
  "use_original_formulation": false
}

State:
  step: None
  num_inference_steps: None
  timestep: None
  count_prepared: 0
  enabled: True
  num_conditions: 2
```

To update guider configuration, you can run `pipe.guider = pipe.guider.new(...)`

```py
pipe.guider = pipe.guider.new(guidance_scale=5.0)
```

Read more on Guider [here](../../using-diffusers/guiders).

## HunyuanVideo15Pipeline[[diffusers.HunyuanVideo15Pipeline]]

#### diffusers.HunyuanVideo15Pipeline[[diffusers.HunyuanVideo15Pipeline]]

```python
diffusers.HunyuanVideo15Pipeline(text_encoder: Qwen2_5_VLTextModel, tokenizer: Qwen2Tokenizer, transformer: HunyuanVideo15Transformer3DModel, vae: AutoencoderKLHunyuanVideo15, scheduler: FlowMatchEulerDiscreteScheduler, text_encoder_2: T5EncoderModel, tokenizer_2: ByT5Tokenizer, guider: ClassifierFreeGuidance)
```

[Source](https://github.com/huggingface/diffusers/blob/vr_14421/src/diffusers/pipelines/hunyuan_video1_5/pipeline_hunyuan_video1_5.py#L166)

**Parameters:**

transformer ([HunyuanVideo15Transformer3DModel](/docs/diffusers/pr_14421/en/api/models/hunyuan_video15_transformer_3d#diffusers.HunyuanVideo15Transformer3DModel)) : Conditional Transformer (MMDiT) architecture to denoise the encoded video latents.

scheduler ([FlowMatchEulerDiscreteScheduler](/docs/diffusers/pr_14421/en/api/schedulers/flow_match_euler_discrete#diffusers.FlowMatchEulerDiscreteScheduler)) : A scheduler to be used in combination with `transformer` to denoise the encoded video latents.

vae ([AutoencoderKLHunyuanVideo15](/docs/diffusers/pr_14421/en/api/models/autoencoder_kl_hunyuan_video15#diffusers.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](/docs/diffusers/pr_14421/en/api/modular_diffusers/guiders#diffusers.ClassifierFreeGuidance)) : [ClassifierFreeGuidance]for classifier free guidance.

Pipeline for text-to-video generation using HunyuanVideo1.5.

This model inherits from [DiffusionPipeline](/docs/diffusers/pr_14421/en/api/pipelines/overview#diffusers.DiffusionPipeline). Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).

#### __call__[[diffusers.HunyuanVideo15Pipeline.__call__]]

```python
__call__(prompt: str | list[str] = None, negative_prompt: str | list[str] = None, height: int | None = None, width: int | None = None, num_frames: int = 121, num_inference_steps: int = 50, sigmas: list = None, num_videos_per_prompt: int | None = 1, generator: typing.Union[torch.Generator, list[torch.Generator], NoneType] = None, latents: typing.Optional[torch.Tensor] = None, prompt_embeds: typing.Optional[torch.Tensor] = None, prompt_embeds_mask: typing.Optional[torch.Tensor] = None, negative_prompt_embeds: typing.Optional[torch.Tensor] = None, negative_prompt_embeds_mask: typing.Optional[torch.Tensor] = None, prompt_embeds_2: typing.Optional[torch.Tensor] = None, prompt_embeds_mask_2: typing.Optional[torch.Tensor] = None, negative_prompt_embeds_2: typing.Optional[torch.Tensor] = None, negative_prompt_embeds_mask_2: typing.Optional[torch.Tensor] = None, output_type: str | None = 'np', return_dict: bool = True, attention_kwargs: dict[str, typing.Any] | None = None)
```

[Source](https://github.com/huggingface/diffusers/blob/vr_14421/src/diffusers/pipelines/hunyuan_video1_5/pipeline_hunyuan_video1_5.py#L542)

**Parameters:**

prompt (`str` or `list[str]`, *optional*) : The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds` instead.

negative_prompt (`str` or `list[str]`, *optional*) : The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead.

height (`int`, *optional*) : The height in pixels of the generated video.

width (`int`, *optional*) : The width in pixels of the generated video.

num_frames (`int`, defaults to `121`) : 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 video 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.

num_videos_per_prompt (`int`, *optional*, defaults to 1) : The number of videos 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 video 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.

prompt_embeds_mask (`torch.Tensor`, *optional*) : Pre-generated mask for prompt embeddings.

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.

negative_prompt_embeds_mask (`torch.Tensor`, *optional*) : Pre-generated mask for negative prompt embeddings.

prompt_embeds_2 (`torch.Tensor`, *optional*) : Pre-generated text embeddings from the second text encoder. Can be used to easily tweak text inputs.

prompt_embeds_mask_2 (`torch.Tensor`, *optional*) : Pre-generated mask for prompt embeddings from the second text encoder.

negative_prompt_embeds_2 (`torch.Tensor`, *optional*) : Pre-generated negative text embeddings from the second text encoder.

negative_prompt_embeds_mask_2 (`torch.Tensor`, *optional*) : Pre-generated mask for negative prompt embeddings from the second text encoder.

output_type (`str`, *optional*, defaults to `"np"`) : The output format of the generated video. Choose between "np", "pt", or "latent".

return_dict (`bool`, *optional*, defaults to `True`) : Whether or not to return a `HunyuanVideo15PipelineOutput` 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).

**Returns:** `~HunyuanVideo15PipelineOutput` or `tuple`

If `return_dict` is `True`, `HunyuanVideo15PipelineOutput` is returned, otherwise a `tuple` is
returned where the first element is a list with the generated videos.

The call function to the pipeline for generation.

Examples:
```python
>>> import torch
>>> from diffusers import HunyuanVideo15Pipeline
>>> from diffusers.utils import export_to_video

>>> model_id = "hunyuanvideo-community/HunyuanVideo-1.5-480p_t2v"
>>> 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)
```

#### encode_prompt[[diffusers.HunyuanVideo15Pipeline.encode_prompt]]

```python
encode_prompt(prompt: str | list[str], device: typing.Optional[torch.device] = None, dtype: typing.Optional[torch.dtype] = None, batch_size: int = 1, num_videos_per_prompt: int = 1, prompt_embeds: typing.Optional[torch.Tensor] = None, prompt_embeds_mask: typing.Optional[torch.Tensor] = None, prompt_embeds_2: typing.Optional[torch.Tensor] = None, prompt_embeds_mask_2: typing.Optional[torch.Tensor] = None)
```

[Source](https://github.com/huggingface/diffusers/blob/vr_14421/src/diffusers/pipelines/hunyuan_video1_5/pipeline_hunyuan_video1_5.py#L334)

**Parameters:**

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.

#### prepare_cond_latents_and_mask[[diffusers.HunyuanVideo15Pipeline.prepare_cond_latents_and_mask]]

```python
prepare_cond_latents_and_mask(latents, dtype: typing.Optional[torch.dtype], device: typing.Optional[torch.device])
```

[Source](https://github.com/huggingface/diffusers/blob/vr_14421/src/diffusers/pipelines/hunyuan_video1_5/pipeline_hunyuan_video1_5.py#L508)

**Parameters:**

latents : Main latents tensor (B, C, F, H, W)

**Returns:** `tuple`

(cond_latents_concat, mask_concat) - both are zero tensors for t2v

Prepare conditional latents and mask for t2v generation.

## HunyuanVideo15ImageToVideoPipeline[[diffusers.HunyuanVideo15ImageToVideoPipeline]]

#### diffusers.HunyuanVideo15ImageToVideoPipeline[[diffusers.HunyuanVideo15ImageToVideoPipeline]]

```python
diffusers.HunyuanVideo15ImageToVideoPipeline(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: SiglipImageProcessorPil)
```

[Source](https://github.com/huggingface/diffusers/blob/vr_14421/src/diffusers/pipelines/hunyuan_video1_5/pipeline_hunyuan_video1_5_image2video.py#L193)

**Parameters:**

transformer ([HunyuanVideo15Transformer3DModel](/docs/diffusers/pr_14421/en/api/models/hunyuan_video15_transformer_3d#diffusers.HunyuanVideo15Transformer3DModel)) : Conditional Transformer (MMDiT) architecture to denoise the encoded video latents.

scheduler ([FlowMatchEulerDiscreteScheduler](/docs/diffusers/pr_14421/en/api/schedulers/flow_match_euler_discrete#diffusers.FlowMatchEulerDiscreteScheduler)) : A scheduler to be used in combination with `transformer` to denoise the encoded video latents.

vae ([AutoencoderKLHunyuanVideo15](/docs/diffusers/pr_14421/en/api/models/autoencoder_kl_hunyuan_video15#diffusers.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](/docs/diffusers/pr_14421/en/api/modular_diffusers/guiders#diffusers.ClassifierFreeGuidance)) : [ClassifierFreeGuidance]for classifier free guidance.

image_encoder (`SiglipVisionModel`) : [SiglipVisionModel](https://huggingface.co/docs/transformers/en/model_doc/siglip#transformers.SiglipVisionModel) variant.

feature_extractor (`SiglipImageProcessor`) : [SiglipImageProcessor](https://huggingface.co/docs/transformers/en/model_doc/siglip#transformers.SiglipImageProcessor) variant.

Pipeline for image-to-video generation using HunyuanVideo1.5.

This model inherits from [DiffusionPipeline](/docs/diffusers/pr_14421/en/api/pipelines/overview#diffusers.DiffusionPipeline). Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).

#### __call__[[diffusers.HunyuanVideo15ImageToVideoPipeline.__call__]]

```python
__call__(image: Image, prompt: str | list[str] = None, negative_prompt: str | list[str] = None, num_frames: int = 121, num_inference_steps: int = 50, sigmas: list = None, num_videos_per_prompt: int | None = 1, generator: typing.Union[torch.Generator, list[torch.Generator], NoneType] = None, latents: typing.Optional[torch.Tensor] = None, prompt_embeds: typing.Optional[torch.Tensor] = None, prompt_embeds_mask: typing.Optional[torch.Tensor] = None, negative_prompt_embeds: typing.Optional[torch.Tensor] = None, negative_prompt_embeds_mask: typing.Optional[torch.Tensor] = None, prompt_embeds_2: typing.Optional[torch.Tensor] = None, prompt_embeds_mask_2: typing.Optional[torch.Tensor] = None, negative_prompt_embeds_2: typing.Optional[torch.Tensor] = None, negative_prompt_embeds_mask_2: typing.Optional[torch.Tensor] = None, output_type: str | None = 'np', return_dict: bool = True, attention_kwargs: dict[str, typing.Any] | None = None)
```

[Source](https://github.com/huggingface/diffusers/blob/vr_14421/src/diffusers/pipelines/hunyuan_video1_5/pipeline_hunyuan_video1_5_image2video.py#L650)

**Parameters:**

image (`PIL.Image.Image`) : The input image to condition video generation on.

prompt (`str` or `list[str]`, *optional*) : The prompt or prompts to guide the video generation. If not defined, one has to pass `prompt_embeds` instead.

negative_prompt (`str` or `list[str]`, *optional*) : The prompt or prompts not to guide the video generation. If not defined, one has to pass `negative_prompt_embeds` instead.

num_frames (`int`, defaults to `121`) : 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 video 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.

num_videos_per_prompt (`int`, *optional*, defaults to 1) : The number of videos 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 video 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.

prompt_embeds_mask (`torch.Tensor`, *optional*) : Pre-generated mask for prompt embeddings.

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.

negative_prompt_embeds_mask (`torch.Tensor`, *optional*) : Pre-generated mask for negative prompt embeddings.

prompt_embeds_2 (`torch.Tensor`, *optional*) : Pre-generated text embeddings from the second text encoder. Can be used to easily tweak text inputs.

prompt_embeds_mask_2 (`torch.Tensor`, *optional*) : Pre-generated mask for prompt embeddings from the second text encoder.

negative_prompt_embeds_2 (`torch.Tensor`, *optional*) : Pre-generated negative text embeddings from the second text encoder.

negative_prompt_embeds_mask_2 (`torch.Tensor`, *optional*) : Pre-generated mask for negative prompt embeddings from the second text encoder.

output_type (`str`, *optional*, defaults to `"np"`) : The output format of the generated video. Choose between "np", "pt", or "latent".

return_dict (`bool`, *optional*, defaults to `True`) : Whether or not to return a `HunyuanVideo15PipelineOutput` 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).

**Returns:** `~HunyuanVideo15PipelineOutput` or `tuple`

If `return_dict` is `True`, `HunyuanVideo15PipelineOutput` is returned, otherwise a `tuple` is
returned where the first element is a list with the generated videos.

The call function to the pipeline for generation.

Examples:
```python
>>> import torch
>>> from diffusers import HunyuanVideo15ImageToVideoPipeline
>>> from diffusers.utils import export_to_video

>>> model_id = "hunyuanvideo-community/HunyuanVideo-1.5-480p_i2v"
>>> pipe = HunyuanVideo15ImageToVideoPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
>>> pipe.vae.enable_tiling()
>>> pipe.to("cuda")

>>> image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/wan_i2v_input.JPG")

>>> output = pipe(
...     prompt="Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside.",
...     image=image,
...     num_inference_steps=50,
... ).frames[0]
>>> export_to_video(output, "output.mp4", fps=24)
```

#### encode_prompt[[diffusers.HunyuanVideo15ImageToVideoPipeline.encode_prompt]]

```python
encode_prompt(prompt: str | list[str], device: typing.Optional[torch.device] = None, dtype: typing.Optional[torch.dtype] = None, batch_size: int = 1, num_videos_per_prompt: int = 1, prompt_embeds: typing.Optional[torch.Tensor] = None, prompt_embeds_mask: typing.Optional[torch.Tensor] = None, prompt_embeds_2: typing.Optional[torch.Tensor] = None, prompt_embeds_mask_2: typing.Optional[torch.Tensor] = None)
```

[Source](https://github.com/huggingface/diffusers/blob/vr_14421/src/diffusers/pipelines/hunyuan_video1_5/pipeline_hunyuan_video1_5_image2video.py#L422)

**Parameters:**

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.

#### prepare_cond_latents_and_mask[[diffusers.HunyuanVideo15ImageToVideoPipeline.prepare_cond_latents_and_mask]]

```python
prepare_cond_latents_and_mask(latents: Tensor, image: Image, batch_size: int, height: int, width: int, dtype: dtype, device: device)
```

[Source](https://github.com/huggingface/diffusers/blob/vr_14421/src/diffusers/pipelines/hunyuan_video1_5/pipeline_hunyuan_video1_5_image2video.py#L594)

**Parameters:**

latents : Main latents tensor (B, C, F, H, W)

**Returns:** `tuple`

(cond_latents_concat, mask_concat) - both are zero tensors for t2v

Prepare conditional latents and mask for t2v generation.

## HunyuanVideo15PipelineOutput[[diffusers.pipelines.hunyuan_video1_5.pipeline_output.HunyuanVideo15PipelineOutput]]

#### diffusers.pipelines.hunyuan_video1_5.pipeline_output.HunyuanVideo15PipelineOutput[[diffusers.pipelines.hunyuan_video1_5.pipeline_output.HunyuanVideo15PipelineOutput]]

```python
diffusers.pipelines.hunyuan_video1_5.pipeline_output.HunyuanVideo15PipelineOutput(frames: Tensor)
```

[Source](https://github.com/huggingface/diffusers/blob/vr_14421/src/diffusers/pipelines/hunyuan_video1_5/pipeline_output.py#L9)

**Parameters:**

frames (`torch.Tensor`, `np.ndarray`, or list[list[PIL.Image.Image]]) : List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing denoised PIL image sequences of length `num_frames.` It can also be a NumPy array or Torch tensor of shape `(batch_size, num_frames, channels, height, width)`.

Output class for HunyuanVideo1.5 pipelines.