Helios

Helios: Real Real-Time Long Video Generation Model from Peking University & ByteDance & etc, by Shenghai Yuan, Yuanyang Yin, Zongjian Li, Xinwei Huang, Xiao Yang, Li Yuan.

We introduce Helios, the first 14B video generation model that runs at 17 FPS on a single NVIDIA H100 GPU and supports minute-scale generation while matching a strong baseline in quality. We make breakthroughs along three key dimensions: (1) robustness to long-video drifting without commonly used anti-drift heuristics such as self-forcing, error banks, or keyframe sampling; (2) real-time generation without standard acceleration techniques such as KV-cache, causal masking, or sparse attention; and (3) training without parallelism or sharding frameworks, enabling image-diffusion-scale batch sizes while fitting up to four 14B models within 80 GB of GPU memory. Specifically, Helios is a 14B autoregressive diffusion model with a unified input representation that natively supports T2V, I2V, and V2V tasks. To mitigate drifting in long-video generation, we characterize its typical failure modes and propose simple yet effective training strategies that explicitly simulate drifting during training, while eliminating repetitive motion at its source. For efficiency, we heavily compress the historical and noisy context and reduce the number of sampling steps, yielding computational costs comparable to—or lower than—those of 1.3B video generative models. Moreover, we introduce infrastructure-level optimizations that accelerate both inference and training while reducing memory consumption. Extensive experiments demonstrate that Helios consistently outperforms prior methods on both short- and long-video generation. All the code and models are available at [this https URL](https://pku-yuangroup.github.io/Helios-Page).

The following Helios models are supported in Diffusers:

Helios-Base: Best Quality, with v-prediction, standard CFG and custom HeliosScheduler.
Helios-Mid: Intermediate Weight, with v-prediction, CFG-Zero* and custom HeliosScheduler.
Helios-Distilled: Best Efficiency, with x0-prediction and custom HeliosDMDScheduler.

Click on the Helios models in the right sidebar for more examples of video generation.

Optimizing Memory and Inference Speed

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

memory

inference speed

Generation with Helios-Base

The example below demonstrates how to use Helios-Base to generate video based on text, image or video.

usage

import torch
from diffusers import AutoModel, HeliosPipeline
from diffusers.utils import export_to_video, load_video, load_image

vae = AutoModel.from_pretrained("BestWishYsh/Helios-Base", subfolder="vae", torch_dtype=torch.float32)

pipeline = HeliosPipeline.from_pretrained(
    "BestWishYsh/Helios-Base",
    vae=vae,
    torch_dtype=torch.bfloat16
)
pipeline.to("cuda")

negative_prompt = """
Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality,
low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured,
misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards
"""

# For Text-to-Video
prompt = """
A vibrant tropical fish swimming gracefully among colorful coral reefs in a clear, turquoise ocean. The fish has bright blue 
and yellow scales with a small, distinctive orange spot on its side, its fins moving fluidly. The coral reefs are alive with 
a variety of marine life, including small schools of colorful fish and sea turtles gliding by. The water is crystal clear, 
allowing for a view of the sandy ocean floor below. The reef itself is adorned with a mix of hard and soft corals in shades 
of red, orange, and green. The photo captures the fish from a slightly elevated angle, emphasizing its lively movements and 
the vivid colors of its surroundings. A close-up shot with dynamic movement.
"""

output = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    num_frames=99,
    num_inference_steps=50,
    guidance_scale=5.0,
    generator=torch.Generator("cuda").manual_seed(42),
).frames[0]
export_to_video(output, "helios_base_t2v_output.mp4", fps=24)

# For Image-to-Video
prompt = """
A towering emerald wave surges forward, its crest curling with raw power and energy. Sunlight glints off the translucent water, 
illuminating the intricate textures and deep green hues within the wave’s body. A thick spray erupts from the breaking crest, 
casting a misty veil that dances above the churning surface. As the perspective widens, the immense scale of the wave becomes 
apparent, revealing the restless expanse of the ocean stretching beyond. The scene captures the ocean’s untamed beauty and 
relentless force, with every droplet and ripple shimmering in the light. The dynamic motion and vivid colors evoke both awe and 
respect for nature’s might.
"""
image_path = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/wave.jpg"

output = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    image=load_image(image_path).resize((640, 384)),
    num_frames=99,
    num_inference_steps=50,
    guidance_scale=5.0,
    generator=torch.Generator("cuda").manual_seed(42),
).frames[0]
export_to_video(output, "helios_base_i2v_output.mp4", fps=24)

# For Video-to-Video
prompt = """
A bright yellow Lamborghini Huracn Tecnica speeds along a curving mountain road, surrounded by lush green trees 
under a partly cloudy sky. The car's sleek design and vibrant color stand out against the natural backdrop, 
emphasizing its dynamic movement. The road curves gently, with a guardrail visible on one side, adding depth to 
the scene. The motion blur captures the sense of speed and energy, creating a thrilling and exhilarating atmosphere. 
A front-facing shot from a slightly elevated angle, highlighting the car's aggressive stance and the surrounding greenery.
"""
video_path = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/car.mp4"

output = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    video=load_video(video_path),
    num_frames=99,
    num_inference_steps=50,
    guidance_scale=5.0,
    generator=torch.Generator("cuda").manual_seed(42),
).frames[0]
export_to_video(output, "helios_base_v2v_output.mp4", fps=24)

Generation with Helios-Mid

The example below demonstrates how to use Helios-Mid to generate video based on text, image or video.

usage

import torch
from diffusers import AutoModel, HeliosPyramidPipeline
from diffusers.utils import export_to_video, load_video, load_image

vae = AutoModel.from_pretrained("BestWishYsh/Helios-Mid", subfolder="vae", torch_dtype=torch.float32)

pipeline = HeliosPyramidPipeline.from_pretrained(
    "BestWishYsh/Helios-Mid",
    vae=vae,
    torch_dtype=torch.bfloat16
)
pipeline.to("cuda")

negative_prompt = """
Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality,
low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured,
misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards
"""

# For Text-to-Video
prompt = """
A vibrant tropical fish swimming gracefully among colorful coral reefs in a clear, turquoise ocean. The fish has bright blue 
and yellow scales with a small, distinctive orange spot on its side, its fins moving fluidly. The coral reefs are alive with 
a variety of marine life, including small schools of colorful fish and sea turtles gliding by. The water is crystal clear, 
allowing for a view of the sandy ocean floor below. The reef itself is adorned with a mix of hard and soft corals in shades 
of red, orange, and green. The photo captures the fish from a slightly elevated angle, emphasizing its lively movements and 
the vivid colors of its surroundings. A close-up shot with dynamic movement.
"""

output = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    num_frames=99,
    pyramid_num_inference_steps_list=[20, 20, 20],
    guidance_scale=5.0,
    use_zero_init=True,
    zero_steps=1,
    generator=torch.Generator("cuda").manual_seed(42),
).frames[0]
export_to_video(output, "helios_pyramid_t2v_output.mp4", fps=24)

# For Image-to-Video
prompt = """
A towering emerald wave surges forward, its crest curling with raw power and energy. Sunlight glints off the translucent water, 
illuminating the intricate textures and deep green hues within the wave’s body. A thick spray erupts from the breaking crest, 
casting a misty veil that dances above the churning surface. As the perspective widens, the immense scale of the wave becomes 
apparent, revealing the restless expanse of the ocean stretching beyond. The scene captures the ocean’s untamed beauty and 
relentless force, with every droplet and ripple shimmering in the light. The dynamic motion and vivid colors evoke both awe and 
respect for nature’s might.
"""
image_path = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/wave.jpg"

output = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    image=load_image(image_path).resize((640, 384)),
    num_frames=99,
    pyramid_num_inference_steps_list=[20, 20, 20],
    guidance_scale=5.0,
    use_zero_init=True,
    zero_steps=1,
    generator=torch.Generator("cuda").manual_seed(42),
).frames[0]
export_to_video(output, "helios_pyramid_i2v_output.mp4", fps=24)

# For Video-to-Video
prompt = """
A bright yellow Lamborghini Huracn Tecnica speeds along a curving mountain road, surrounded by lush green trees 
under a partly cloudy sky. The car's sleek design and vibrant color stand out against the natural backdrop, 
emphasizing its dynamic movement. The road curves gently, with a guardrail visible on one side, adding depth to 
the scene. The motion blur captures the sense of speed and energy, creating a thrilling and exhilarating atmosphere. 
A front-facing shot from a slightly elevated angle, highlighting the car's aggressive stance and the surrounding greenery.
"""
video_path = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/car.mp4"

output = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    video=load_video(video_path),
    num_frames=99,
    pyramid_num_inference_steps_list=[20, 20, 20],
    guidance_scale=5.0,
    use_zero_init=True,
    zero_steps=1,
    generator=torch.Generator("cuda").manual_seed(42),
).frames[0]
export_to_video(output, "helios_pyramid_v2v_output.mp4", fps=24)

Generation with Helios-Distilled

The example below demonstrates how to use Helios-Distilled to generate video based on text, image or video.

usage

import torch
from diffusers import AutoModel, HeliosPyramidPipeline
from diffusers.utils import export_to_video, load_video, load_image

vae = AutoModel.from_pretrained("BestWishYsh/Helios-Distilled", subfolder="vae", torch_dtype=torch.float32)

pipeline = HeliosPyramidPipeline.from_pretrained(
    "BestWishYsh/Helios-Distilled",
    vae=vae,
    torch_dtype=torch.bfloat16
)
pipeline.to("cuda")

negative_prompt = """
Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality,
low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured,
misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards
"""

# For Text-to-Video
prompt = """
A vibrant tropical fish swimming gracefully among colorful coral reefs in a clear, turquoise ocean. The fish has bright blue 
and yellow scales with a small, distinctive orange spot on its side, its fins moving fluidly. The coral reefs are alive with 
a variety of marine life, including small schools of colorful fish and sea turtles gliding by. The water is crystal clear, 
allowing for a view of the sandy ocean floor below. The reef itself is adorned with a mix of hard and soft corals in shades 
of red, orange, and green. The photo captures the fish from a slightly elevated angle, emphasizing its lively movements and 
the vivid colors of its surroundings. A close-up shot with dynamic movement.
"""

output = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    num_frames=240,
    pyramid_num_inference_steps_list=[2, 2, 2],
    guidance_scale=1.0,
    is_amplify_first_chunk=True,
    generator=torch.Generator("cuda").manual_seed(42),
).frames[0]
export_to_video(output, "helios_distilled_t2v_output.mp4", fps=24)

# For Image-to-Video
prompt = """
A towering emerald wave surges forward, its crest curling with raw power and energy. Sunlight glints off the translucent water, 
illuminating the intricate textures and deep green hues within the wave’s body. A thick spray erupts from the breaking crest, 
casting a misty veil that dances above the churning surface. As the perspective widens, the immense scale of the wave becomes 
apparent, revealing the restless expanse of the ocean stretching beyond. The scene captures the ocean’s untamed beauty and 
relentless force, with every droplet and ripple shimmering in the light. The dynamic motion and vivid colors evoke both awe and 
respect for nature’s might.
"""
image_path = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/wave.jpg"

output = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    image=load_image(image_path).resize((640, 384)),
    num_frames=240,
    pyramid_num_inference_steps_list=[2, 2, 2],
    guidance_scale=1.0,
    is_amplify_first_chunk=True,
    generator=torch.Generator("cuda").manual_seed(42),
).frames[0]
export_to_video(output, "helios_distilled_i2v_output.mp4", fps=24)

# For Video-to-Video
prompt = """
A bright yellow Lamborghini Huracn Tecnica speeds along a curving mountain road, surrounded by lush green trees 
under a partly cloudy sky. The car's sleek design and vibrant color stand out against the natural backdrop, 
emphasizing its dynamic movement. The road curves gently, with a guardrail visible on one side, adding depth to 
the scene. The motion blur captures the sense of speed and energy, creating a thrilling and exhilarating atmosphere. 
A front-facing shot from a slightly elevated angle, highlighting the car's aggressive stance and the surrounding greenery.
"""
video_path = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/car.mp4"

output = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    video=load_video(video_path),
    num_frames=240,
    pyramid_num_inference_steps_list=[2, 2, 2],
    guidance_scale=1.0,
    is_amplify_first_chunk=True,
    generator=torch.Generator("cuda").manual_seed(42),
).frames[0]
export_to_video(output, "helios_distilled_v2v_output.mp4", fps=24)

HeliosPipeline

class diffusers.HeliosPipeline

< source >

( tokenizer: AutoTokenizer text_encoder: UMT5EncoderModel vae: AutoencoderKLWan scheduler: HeliosScheduler transformer: HeliosTransformer3DModel )

Parameters

tokenizer (T5Tokenizer) — Tokenizer from T5, specifically the google/umt5-xxl variant.
text_encoder (T5EncoderModel) — T5, specifically the google/umt5-xxl variant.
transformer (HeliosTransformer3DModel) — Conditional Transformer to denoise the input latents.
scheduler (HeliosScheduler) — A scheduler to be used in combination with transformer to denoise the encoded image latents.
vae (AutoencoderKLWan) — Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.

Pipeline for text-to-video / image-to-video / video-to-video generation using Helios.

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.).

call

< source >

( prompt: str | list[str] = None negative_prompt: str | list[str] = None height: int = 384 width: int = 640 num_frames: int = 132 num_inference_steps: int = 50 sigmas: list = None guidance_scale: float = 5.0 num_videos_per_prompt: int | None = 1 generator: torch._C.Generator | list[torch._C.Generator] | None = None latents: torch.Tensor | None = None prompt_embeds: torch.Tensor | None = None negative_prompt_embeds: torch.Tensor | None = None output_type: str | None = 'np' return_dict: bool = True attention_kwargs: dict[str, typing.Any] | None = None callback_on_step_end: typing.Union[typing.Callable[[int, int], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None callback_on_step_end_tensor_inputs: list = ['latents'] max_sequence_length: int = 512 image: PIL.Image.Image | numpy.ndarray | torch.Tensor | list[PIL.Image.Image] | list[numpy.ndarray] | list[torch.Tensor] | None = None image_latents: torch.Tensor | None = None fake_image_latents: torch.Tensor | None = None add_noise_to_image_latents: bool = True image_noise_sigma_min: float = 0.111 image_noise_sigma_max: float = 0.135 video: PIL.Image.Image | numpy.ndarray | torch.Tensor | list[PIL.Image.Image] | list[numpy.ndarray] | list[torch.Tensor] | None = None video_latents: torch.Tensor | None = None add_noise_to_video_latents: bool = True video_noise_sigma_min: float = 0.111 video_noise_sigma_max: float = 0.135 history_sizes: list = [16, 2, 1] num_latent_frames_per_chunk: int = 9 keep_first_frame: bool = True is_skip_first_chunk: bool = False ) → ~HeliosPipelineOutput or tuple

Parameters

prompt (str or list[str], optional) — The prompt or prompts to guide the image generation. If not defined, pass prompt_embeds instead.
negative_prompt (str or list[str], optional) — The prompt or prompts to avoid during image generation. If not defined, pass negative_prompt_embeds instead. Ignored when not using guidance (guidance_scale < 1).
height (int, defaults to 384) — The height in pixels of the generated image.
width (int, defaults to 640) — The width in pixels of the generated image.
num_frames (int, defaults to 132) — 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.
guidance_scale (float, defaults to 5.0) — Guidance scale as defined in Classifier-Free Diffusion Guidance. guidance_scale is defined as w of equation 2. of Imagen Paper. Guidance scale is enabled by setting guidance_scale > 1. Higher guidance scale encourages to generate images that are closely linked to the text prompt, usually at the expense of lower image quality.
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 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.
output_type (str, optional, defaults to "np") — 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 HeliosPipelineOutput 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.
callback_on_step_end (Callable, PipelineCallback, MultiPipelineCallbacks, optional) — A function or a subclass of PipelineCallback or MultiPipelineCallbacks that is called at the end of each denoising step during the inference. with the following arguments: callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict). callback_kwargs will include a list of all tensors as specified by callback_on_step_end_tensor_inputs.
callback_on_step_end_tensor_inputs (list, optional) — The list of tensor inputs for the callback_on_step_end function. The tensors specified in the list will be passed as callback_kwargs argument. You will only be able to include variables listed in the ._callback_tensor_inputs attribute of your pipeline class.
max_sequence_length (int, defaults to 512) — The maximum sequence length of the text encoder. If the prompt is longer than this, it will be truncated. If the prompt is shorter, it will be padded to this length.

Returns

~HeliosPipelineOutput or tuple

If return_dict is True, HeliosPipelineOutput 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 bools indicating whether the corresponding generated image contains “not-safe-for-work” (nsfw) content.

The call function to the pipeline for generation.

Examples:

>>> import torch
>>> from diffusers.utils import export_to_video
>>> from diffusers import AutoencoderKLWan, HeliosPipeline

>>> # Available models: BestWishYsh/Helios-Base, BestWishYsh/Helios-Mid, BestWishYsh/Helios-Distilled
>>> model_id = "BestWishYsh/Helios-Base"
>>> vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
>>> pipe = HeliosPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
>>> pipe.to("cuda")

>>> prompt = "A cat and a dog baking a cake together in a kitchen. The cat is carefully measuring flour, while the dog is stirring the batter with a wooden spoon. The kitchen is cozy, with sunlight streaming through the window."
>>> negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"

>>> output = pipe(
...     prompt=prompt,
...     negative_prompt=negative_prompt,
...     height=384,
...     width=640,
...     num_frames=132,
...     guidance_scale=5.0,
... ).frames[0]
>>> export_to_video(output, "output.mp4", fps=24)

encode_prompt

< source >

Parameters

prompt (str or list[str], optional) — prompt to be encoded
negative_prompt (str or list[str], optional) — The prompt or prompts not to guide the image generation. If not defined, one has to pass negative_prompt_embeds instead. Ignored when not using guidance (i.e., ignored if guidance_scale is less than 1).
do_classifier_free_guidance (bool, optional, defaults to True) — Whether to use classifier free guidance or not.
num_videos_per_prompt (int, optional, defaults to 1) — Number of videos that should be generated per prompt. torch device to place the resulting embeddings on
prompt_embeds (torch.Tensor, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, text embeddings will be generated from prompt input argument.
negative_prompt_embeds (torch.Tensor, optional) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, negative_prompt_embeds will be generated from negative_prompt input argument.
device — (torch.device, optional): torch device
dtype — (torch.dtype, optional): torch dtype

Encodes the prompt into text encoder hidden states.

HeliosPyramidPipeline

class diffusers.HeliosPyramidPipeline

< source >

( tokenizer: AutoTokenizer text_encoder: UMT5EncoderModel vae: AutoencoderKLWan scheduler: diffusers.schedulers.scheduling_helios.HeliosScheduler | diffusers.schedulers.scheduling_helios_dmd.HeliosDMDScheduler transformer: HeliosTransformer3DModel is_cfg_zero_star: bool = False is_distilled: bool = False )

Parameters

tokenizer (T5Tokenizer) — Tokenizer from T5, specifically the google/umt5-xxl variant.
text_encoder (T5EncoderModel) — T5, specifically the google/umt5-xxl variant.
transformer (HeliosTransformer3DModel) — Conditional Transformer to denoise the input latents.
scheduler ([HeliosScheduler, HeliosDMDScheduler]) — A scheduler to be used in combination with transformer to denoise the encoded image latents.
vae (AutoencoderKLWan) — Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.

Pipeline for text-to-video / image-to-video / video-to-video generation using Helios.

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.).

call

< source >

( prompt: str | list[str] = None negative_prompt: str | list[str] = None height: int = 384 width: int = 640 num_frames: int = 132 sigmas: list = None guidance_scale: float = 5.0 num_videos_per_prompt: int | None = 1 generator: torch._C.Generator | list[torch._C.Generator] | None = None latents: torch.Tensor | None = None prompt_embeds: torch.Tensor | None = None negative_prompt_embeds: torch.Tensor | None = None output_type: str | None = 'np' return_dict: bool = True attention_kwargs: dict[str, typing.Any] | None = None callback_on_step_end: typing.Union[typing.Callable[[int, int], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None callback_on_step_end_tensor_inputs: list = ['latents'] max_sequence_length: int = 512 image: PIL.Image.Image | numpy.ndarray | torch.Tensor | list[PIL.Image.Image] | list[numpy.ndarray] | list[torch.Tensor] | None = None image_latents: torch.Tensor | None = None fake_image_latents: torch.Tensor | None = None add_noise_to_image_latents: bool = True image_noise_sigma_min: float = 0.111 image_noise_sigma_max: float = 0.135 video: PIL.Image.Image | numpy.ndarray | torch.Tensor | list[PIL.Image.Image] | list[numpy.ndarray] | list[torch.Tensor] | None = None video_latents: torch.Tensor | None = None add_noise_to_video_latents: bool = True video_noise_sigma_min: float = 0.111 video_noise_sigma_max: float = 0.135 history_sizes: list = [16, 2, 1] num_latent_frames_per_chunk: int = 9 keep_first_frame: bool = True is_skip_first_chunk: bool = False pyramid_num_inference_steps_list: list = [10, 10, 10] use_zero_init: bool | None = True zero_steps: int | None = 1 is_amplify_first_chunk: bool = False ) → ~HeliosPipelineOutput or tuple

Parameters

prompt (str or list[str], optional) — The prompt or prompts to guide the image generation. If not defined, pass prompt_embeds instead.
negative_prompt (str or list[str], optional) — The prompt or prompts to avoid during image generation. If not defined, pass negative_prompt_embeds instead. Ignored when not using guidance (guidance_scale < 1).
height (int, defaults to 384) — The height in pixels of the generated image.
width (int, defaults to 640) — The width in pixels of the generated image.
num_frames (int, defaults to 132) — The number of frames in the generated video.
guidance_scale (float, defaults to 5.0) — Guidance scale as defined in Classifier-Free Diffusion Guidance. guidance_scale is defined as w of equation 2. of Imagen Paper. Guidance scale is enabled by setting guidance_scale > 1. Higher guidance scale encourages to generate images that are closely linked to the text prompt, usually at the expense of lower image quality.
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 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.
output_type (str, optional, defaults to "np") — 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 HeliosPipelineOutput 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.
callback_on_step_end (Callable, PipelineCallback, MultiPipelineCallbacks, optional) — A function or a subclass of PipelineCallback or MultiPipelineCallbacks that is called at the end of each denoising step during the inference. with the following arguments: callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict). callback_kwargs will include a list of all tensors as specified by callback_on_step_end_tensor_inputs.
callback_on_step_end_tensor_inputs (list, optional) — The list of tensor inputs for the callback_on_step_end function. The tensors specified in the list will be passed as callback_kwargs argument. You will only be able to include variables listed in the ._callback_tensor_inputs attribute of your pipeline class.
max_sequence_length (int, defaults to 512) — The maximum sequence length of the text encoder. If the prompt is longer than this, it will be truncated. If the prompt is shorter, it will be padded to this length.

Returns

~HeliosPipelineOutput or tuple

The call function to the pipeline for generation.

Examples:

>>> import torch
>>> from diffusers.utils import export_to_video
>>> from diffusers import AutoencoderKLWan, HeliosPyramidPipeline

>>> # Available models: BestWishYsh/Helios-Base, BestWishYsh/Helios-Mid, BestWishYsh/Helios-Distilled
>>> model_id = "BestWishYsh/Helios-Base"
>>> vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
>>> pipe = HeliosPyramidPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
>>> pipe.to("cuda")

>>> prompt = "A cat and a dog baking a cake together in a kitchen. The cat is carefully measuring flour, while the dog is stirring the batter with a wooden spoon. The kitchen is cozy, with sunlight streaming through the window."
>>> negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"

>>> output = pipe(
...     prompt=prompt,
...     negative_prompt=negative_prompt,
...     height=384,
...     width=640,
...     num_frames=132,
...     guidance_scale=5.0,
... ).frames[0]
>>> export_to_video(output, "output.mp4", fps=24)

encode_prompt

< source >

Parameters

prompt (str or list[str], optional) — prompt to be encoded
negative_prompt (str or list[str], optional) — The prompt or prompts not to guide the image generation. If not defined, one has to pass negative_prompt_embeds instead. Ignored when not using guidance (i.e., ignored if guidance_scale is less than 1).
do_classifier_free_guidance (bool, optional, defaults to True) — Whether to use classifier free guidance or not.
num_videos_per_prompt (int, optional, defaults to 1) — Number of videos that should be generated per prompt. torch device to place the resulting embeddings on
prompt_embeds (torch.Tensor, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, text embeddings will be generated from prompt input argument.
negative_prompt_embeds (torch.Tensor, optional) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, negative_prompt_embeds will be generated from negative_prompt input argument.
device — (torch.device, optional): torch device
dtype — (torch.dtype, optional): torch dtype

Encodes the prompt into text encoder hidden states.

HeliosPipelineOutput

class diffusers.pipelines.helios.pipeline_output.HeliosPipelineOutput

< source >

( frames: Tensor )

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 Helios pipelines.

Update on GitHub

Diffusers

Helios

Optimizing Memory and Inference Speed

Generation with Helios-Base

Generation with Helios-Mid

Generation with Helios-Distilled

HeliosPipeline

class diffusers.HeliosPipeline

__call__

encode_prompt

HeliosPyramidPipeline

class diffusers.HeliosPyramidPipeline

__call__

encode_prompt

HeliosPipelineOutput

class diffusers.pipelines.helios.pipeline_output.HeliosPipelineOutput

call

call