# # 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", torch_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/main/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]] [Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/hunyuan_video1_5/pipeline_hunyuan_video1_5.py#L166) Pipeline for text-to-video generation using HunyuanVideo1.5. This model inherits from [DiffusionPipeline](/docs/diffusers/main/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__https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/hunyuan_video1_5/pipeline_hunyuan_video1_5.py#L542[{"name": "prompt", "val": ": str | list[str] = None"}, {"name": "negative_prompt", "val": ": str | list[str] = None"}, {"name": "height", "val": ": int | None = None"}, {"name": "width", "val": ": int | None = None"}, {"name": "num_frames", "val": ": int = 121"}, {"name": "num_inference_steps", "val": ": int = 50"}, {"name": "sigmas", "val": ": list = None"}, {"name": "num_videos_per_prompt", "val": ": int | None = 1"}, {"name": "generator", "val": ": torch._C.Generator | list[torch._C.Generator] | None = None"}, {"name": "latents", "val": ": torch.Tensor | None = None"}, {"name": "prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "prompt_embeds_mask", "val": ": torch.Tensor | None = None"}, {"name": "negative_prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "negative_prompt_embeds_mask", "val": ": torch.Tensor | None = None"}, {"name": "prompt_embeds_2", "val": ": torch.Tensor | None = None"}, {"name": "prompt_embeds_mask_2", "val": ": torch.Tensor | None = None"}, {"name": "negative_prompt_embeds_2", "val": ": torch.Tensor | None = None"}, {"name": "negative_prompt_embeds_mask_2", "val": ": torch.Tensor | None = None"}, {"name": "output_type", "val": ": str | None = 'np'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "attention_kwargs", "val": ": dict[str, typing.Any] | None = None"}]- **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).0`~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) ``` **Parameters:** transformer ([HunyuanVideo15Transformer3DModel](/docs/diffusers/main/en/api/models/hunyuan_video15_transformer_3d#diffusers.HunyuanVideo15Transformer3DModel)) : Conditional Transformer (MMDiT) architecture to denoise the encoded video latents. scheduler ([FlowMatchEulerDiscreteScheduler](/docs/diffusers/main/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/main/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/main/en/api/modular_diffusers/guiders#diffusers.ClassifierFreeGuidance)) : [ClassifierFreeGuidance]for classifier free guidance. **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. #### encode_prompt[[diffusers.HunyuanVideo15Pipeline.encode_prompt]] [Source](https://github.com/huggingface/diffusers/blob/main/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]] [Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/hunyuan_video1_5/pipeline_hunyuan_video1_5.py#L508) Prepare conditional latents and mask for t2v generation. **Parameters:** latents : Main latents tensor (B, C, F, H, W) **Returns:** `tuple` (cond_latents_concat, mask_concat) - both are zero tensors for t2v ## HunyuanVideo15ImageToVideoPipeline[[diffusers.HunyuanVideo15ImageToVideoPipeline]] #### diffusers.HunyuanVideo15ImageToVideoPipeline[[diffusers.HunyuanVideo15ImageToVideoPipeline]] [Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/hunyuan_video1_5/pipeline_hunyuan_video1_5_image2video.py#L193) Pipeline for image-to-video generation using HunyuanVideo1.5. This model inherits from [DiffusionPipeline](/docs/diffusers/main/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__https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/hunyuan_video1_5/pipeline_hunyuan_video1_5_image2video.py#L650[{"name": "image", "val": ": Image"}, {"name": "prompt", "val": ": str | list[str] = None"}, {"name": "negative_prompt", "val": ": str | list[str] = None"}, {"name": "num_frames", "val": ": int = 121"}, {"name": "num_inference_steps", "val": ": int = 50"}, {"name": "sigmas", "val": ": list = None"}, {"name": "num_videos_per_prompt", "val": ": int | None = 1"}, {"name": "generator", "val": ": torch._C.Generator | list[torch._C.Generator] | None = None"}, {"name": "latents", "val": ": torch.Tensor | None = None"}, {"name": "prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "prompt_embeds_mask", "val": ": torch.Tensor | None = None"}, {"name": "negative_prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "negative_prompt_embeds_mask", "val": ": torch.Tensor | None = None"}, {"name": "prompt_embeds_2", "val": ": torch.Tensor | None = None"}, {"name": "prompt_embeds_mask_2", "val": ": torch.Tensor | None = None"}, {"name": "negative_prompt_embeds_2", "val": ": torch.Tensor | None = None"}, {"name": "negative_prompt_embeds_mask_2", "val": ": torch.Tensor | None = None"}, {"name": "output_type", "val": ": str | None = 'np'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "attention_kwargs", "val": ": dict[str, typing.Any] | None = None"}]- **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).0`~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) ``` **Parameters:** transformer ([HunyuanVideo15Transformer3DModel](/docs/diffusers/main/en/api/models/hunyuan_video15_transformer_3d#diffusers.HunyuanVideo15Transformer3DModel)) : Conditional Transformer (MMDiT) architecture to denoise the encoded video latents. scheduler ([FlowMatchEulerDiscreteScheduler](/docs/diffusers/main/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/main/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/main/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. **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. #### encode_prompt[[diffusers.HunyuanVideo15ImageToVideoPipeline.encode_prompt]] [Source](https://github.com/huggingface/diffusers/blob/main/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]] [Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/hunyuan_video1_5/pipeline_hunyuan_video1_5_image2video.py#L594) Prepare conditional latents and mask for t2v generation. **Parameters:** latents : Main latents tensor (B, C, F, H, W) **Returns:** `tuple` (cond_latents_concat, mask_concat) - both are zero tensors for t2v ## 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]] [Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/hunyuan_video1_5/pipeline_output.py#L9) Output class for HunyuanVideo1.5 pipelines. **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)`.