🧪 This pipeline is for research purposes only.

#
Text-to-video

VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation is by Zhengxiong Luo, Dayou Chen, Yingya Zhang, Yan Huang, Liang Wang, Yujun Shen, Deli Zhao, Jingren Zhou, Tieniu Tan.

The abstract from the paper is:

A diffusion probabilistic model (DPM), which constructs a forward diffusion process by gradually adding noise to data points and learns the reverse denoising process to generate new samples, has been shown to handle complex data distribution. Despite its recent success in image synthesis, applying DPMs to video generation is still challenging due to high-dimensional data spaces. Previous methods usually adopt a standard diffusion process, where frames in the same video clip are destroyed with independent noises, ignoring the content redundancy and temporal correlation. This work presents a decomposed diffusion process via resolving the per-frame noise into a base noise that is shared among all frames and a residual noise that varies along the time axis. The denoising pipeline employs two jointly-learned networks to match the noise decomposition accordingly. Experiments on various datasets confirm that our approach, termed as VideoFusion, surpasses both GAN-based and diffusion-based alternatives in high-quality video generation. We further show that our decomposed formulation can benefit from pre-trained image diffusion models and well-support text-conditioned video creation.

You can find additional information about Text-to-Video on the project page, original codebase, and try it out in a demo. Official checkpoints can be found at damo-vilab and cerspense.

##
Usage example

###
`text-to-video-ms-1.7b`

Let’s start by generating a short video with the default length of 16 frames (2s at 8 fps):

    import torch
    from diffusers import DiffusionPipeline
    from diffusers.utils import export_to_video

    pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16")
    pipe = pipe.to("cuda")

    prompt = "Spiderman is surfing"
    video_frames = pipe(prompt).frames
    video_path = export_to_video(video_frames)
    video_path

Diffusers supports different optimization techniques to improve the latency and memory footprint of a pipeline. Since videos are often more memory-heavy than images, we can enable CPU offloading and VAE slicing to keep the memory footprint at bay.

Let’s generate a video of 8 seconds (64 frames) on the same GPU using CPU offloading and VAE slicing:

    import torch
    from diffusers import DiffusionPipeline
    from diffusers.utils import export_to_video

    pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16")
    pipe.enable_model_cpu_offload()

    # memory optimization
    pipe.enable_vae_slicing()

    prompt = "Darth Vader surfing a wave"
    video_frames = pipe(prompt, num_frames=64).frames
    video_path = export_to_video(video_frames)
    video_path

It just takes 7 GBs of GPU memory to generate the 64 video frames using PyTorch 2.0, “fp16” precision and the techniques mentioned above.

We can also use a different scheduler easily, using the same method we’d use for Stable Diffusion:

    import torch
    from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
    from diffusers.utils import export_to_video

    pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16")
    pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
    pipe.enable_model_cpu_offload()

    prompt = "Spiderman is surfing"
    video_frames = pipe(prompt, num_inference_steps=25).frames
    video_path = export_to_video(video_frames)
    video_path

Here are some sample outputs:

An astronaut riding a horse. | Darth vader surfing in waves.

###
`cerspense/zeroscope_v2_576w` & `cerspense/zeroscope_v2_XL`

Zeroscope are watermark-free model and have been trained on specific sizes such as `576x320` and `1024x576`. One should first generate a video using the lower resolution checkpoint `cerspense/zeroscope_v2_576w` with TextToVideoSDPipeline, which can then be upscaled using VideoToVideoSDPipeline and `cerspense/zeroscope_v2_XL`.

    import torch
    from diffusers import DiffusionPipeline
    from diffusers.utils import export_to_video

    pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_576w", torch_dtype=torch.float16)
    pipe.enable_model_cpu_offload()

    # memory optimization
    pipe.unet.enable_forward_chunking(chunk_size=1, dim=1)
    pipe.enable_vae_slicing()

    prompt = "Darth Vader surfing a wave"
    video_frames = pipe(prompt, num_frames=24).frames
    video_path = export_to_video(video_frames)
    video_path

Now the video can be upscaled:

    pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_XL", torch_dtype=torch.float16)
    pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
    pipe.enable_model_cpu_offload()

    # memory optimization
    pipe.unet.enable_forward_chunking(chunk_size=1, dim=1)
    pipe.enable_vae_slicing()

    video = [Image.fromarray(frame).resize((1024, 576)) for frame in video_frames]

    video_frames = pipe(prompt, video=video, strength=0.6).frames
    video_path = export_to_video(video_frames)
    video_path

Here are some sample outputs:

Darth vader surfing in waves.

##
TextToVideoSDPipeline

###
class diffusers.TextToVideoSDPipeline

< source >

( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet3DConditionModel scheduler: KarrasDiffusionSchedulers )

Parameters

- vae (AutoencoderKL) — Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
- text_encoder (`CLIPTextModel`) — Frozen text-encoder (clip-vit-large-patch14).
- tokenizer (`CLIPTokenizer`) — A CLIPTokenizer to tokenize text.
- unet (UNet3DConditionModel) — A UNet3DConditionModel to denoise the encoded video latents.
- scheduler (SchedulerMixin) — A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler.

Pipeline for text-to-video generation.

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: typing.Union[str, typing.List[str]] = None height: typing.Optional[int] = None width: typing.Optional[int] = None num_frames: int = 16 num_inference_steps: int = 50 guidance_scale: float = 9.0 negative_prompt: typing.Union[str, typing.List[str], NoneType] = None eta: float = 0.0 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.FloatTensor] = None prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None output_type: typing.Optional[str] = 'np' return_dict: bool = True callback: typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None callback_steps: int = 1 cross_attention_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None ) → TextToVideoSDPipelineOutput or `tuple`

Parameters

- prompt (`str` or `List[str]`, optional) — The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
- height (`int`, optional, defaults to `self.unet.config.sample_size * self.vae_scale_factor`) — The height in pixels of the generated video.
- width (`int`, optional, defaults to `self.unet.config.sample_size * self.vae_scale_factor`) — The width in pixels of the generated video.
- num_frames (`int`, optional, defaults to 16) — The number of video frames that are generated. Defaults to 16 frames which at 8 frames per seconds amounts to 2 seconds of video.
- num_inference_steps (`int`, optional, defaults to 50) — The number of denoising steps. More denoising steps usually lead to a higher quality videos at the expense of slower inference.
- guidance_scale (`float`, optional, defaults to 7.5) — A higher guidance scale value encourages the model to generate images closely linked to the text `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
- negative_prompt (`str` or `List[str]`, optional) — The prompt or prompts to guide what to not include in image generation. If not defined, you need to pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
- num_images_per_prompt (`int`, optional, defaults to 1) — The number of images to generate per prompt.
- eta (`float`, optional, defaults to 0.0) — Corresponds to parameter eta (η) from the DDIM paper. Only applies to the DDIMScheduler, and is ignored in other schedulers.
- generator (`torch.Generator` or `List[torch.Generator]`, optional) — A `torch.Generator` to make generation deterministic.
- latents (`torch.FloatTensor`, 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`. Latents should be of shape `(batch_size, num_channel, num_frames, height, width)`.
- prompt_embeds (`torch.FloatTensor`, 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.
- negative_prompt_embeds (`torch.FloatTensor`, optional) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
- output_type (`str`, optional, defaults to `"np"`) — The output format of the generated video. Choose between `torch.FloatTensor` or `np.array`.
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a TextToVideoSDPipelineOutput instead of a plain tuple.
- callback (`Callable`, optional) — A function that calls every `callback_steps` steps during inference. The function is called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
- callback_steps (`int`, optional, defaults to 1) — The frequency at which the `callback` function is called. If not specified, the callback is called at every step.
- cross_attention_kwargs (`dict`, optional) — A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined in `self.processor`.

Returns

TextToVideoSDPipelineOutput or `tuple`

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

The call function to the pipeline for generation.

Examples:

    >>> import torch
    >>> from diffusers import TextToVideoSDPipeline
    >>> from diffusers.utils import export_to_video

    >>> pipe = TextToVideoSDPipeline.from_pretrained(
    ...     "damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16"
    ... )
    >>> pipe.enable_model_cpu_offload()

    >>> prompt = "Spiderman is surfing"
    >>> video_frames = pipe(prompt).frames
    >>> video_path = export_to_video(video_frames)
    >>> video_path

####
disable_vae_slicing

< source >

( )

Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to computing decoding in one step.

####
disable_vae_tiling

< source >

( )

Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to computing decoding in one step.

####
enable_model_cpu_offload

< source >

( gpu_id = 0 )

Offload all models to CPU to reduce memory usage with a low impact on performance. Moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than using `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.

####
enable_vae_slicing

< source >

( )

Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.

####
enable_vae_tiling

< source >

( )

Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow processing larger images.

##
VideoToVideoSDPipeline

###
class diffusers.VideoToVideoSDPipeline

< source >

( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet3DConditionModel scheduler: KarrasDiffusionSchedulers )

Parameters

- vae (AutoencoderKL) — Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
- text_encoder (`CLIPTextModel`) — Frozen text-encoder (clip-vit-large-patch14).
- tokenizer (`CLIPTokenizer`) — A CLIPTokenizer to tokenize text.
- unet (UNet3DConditionModel) — A UNet3DConditionModel to denoise the encoded video latents.
- scheduler (SchedulerMixin) — A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler.

Pipeline for text-guided video-to-video generation.

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: typing.Union[str, typing.List[str]] = None video: typing.Union[typing.List[numpy.ndarray], torch.FloatTensor] = None strength: float = 0.6 num_inference_steps: int = 50 guidance_scale: float = 15.0 negative_prompt: typing.Union[str, typing.List[str], NoneType] = None eta: float = 0.0 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.FloatTensor] = None prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None output_type: typing.Optional[str] = 'np' return_dict: bool = True callback: typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None callback_steps: int = 1 cross_attention_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None ) → TextToVideoSDPipelineOutput or `tuple`

Parameters

- prompt (`str` or `List[str]`, optional) — The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
- video (`List[np.ndarray]` or `torch.FloatTensor`) — `video` frames or tensor representing a video batch to be used as the starting point for the process. Can also accpet video latents as `image`, if passing latents directly, it will not be encoded again.
- strength (`float`, optional, defaults to 0.8) — Indicates extent to transform the reference `video`. Must be between 0 and 1. `video` is used as a starting point, adding more noise to it the larger the `strength`. The number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising process runs for the full number of iterations specified in `num_inference_steps`. A value of 1 essentially ignores `video`.
- num_inference_steps (`int`, optional, defaults to 50) — The number of denoising steps. More denoising steps usually lead to a higher quality videos at the expense of slower inference.
- guidance_scale (`float`, optional, defaults to 7.5) — A higher guidance scale value encourages the model to generate images closely linked to the text `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
- negative_prompt (`str` or `List[str]`, optional) — The prompt or prompts to guide what to not include in video generation. If not defined, you need to pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
- eta (`float`, optional, defaults to 0.0) — Corresponds to parameter eta (η) from the DDIM paper. Only applies to the DDIMScheduler, and is ignored in other schedulers.
- generator (`torch.Generator` or `List[torch.Generator]`, optional) — A `torch.Generator` to make generation deterministic.
- latents (`torch.FloatTensor`, 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`. Latents should be of shape `(batch_size, num_channel, num_frames, height, width)`.
- prompt_embeds (`torch.FloatTensor`, 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.
- negative_prompt_embeds (`torch.FloatTensor`, optional) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
- output_type (`str`, optional, defaults to `"np"`) — The output format of the generated video. Choose between `torch.FloatTensor` or `np.array`.
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a TextToVideoSDPipelineOutput instead of a plain tuple.
- callback (`Callable`, optional) — A function that calls every `callback_steps` steps during inference. The function is called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
- callback_steps (`int`, optional, defaults to 1) — The frequency at which the `callback` function is called. If not specified, the callback is called at every step.
- cross_attention_kwargs (`dict`, optional) — A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined in `self.processor`.

Returns

TextToVideoSDPipelineOutput or `tuple`

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

The call function to the pipeline for generation.

Examples:

    >>> import torch
    >>> from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
    >>> from diffusers.utils import export_to_video

    >>> pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_576w", torch_dtype=torch.float16)
    >>> pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
    >>> pipe.to("cuda")

    >>> prompt = "spiderman running in the desert"
    >>> video_frames = pipe(prompt, num_inference_steps=40, height=320, width=576, num_frames=24).frames
    >>> # safe low-res video
    >>> video_path = export_to_video(video_frames, output_video_path="./video_576_spiderman.mp4")

    >>> # let's offload the text-to-image model
    >>> pipe.to("cpu")

    >>> # and load the image-to-image model
    >>> pipe = DiffusionPipeline.from_pretrained(
    ...     "cerspense/zeroscope_v2_XL", torch_dtype=torch.float16, revision="refs/pr/15"
    ... )
    >>> pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
    >>> pipe.enable_model_cpu_offload()

    >>> # The VAE consumes A LOT of memory, let's make sure we run it in sliced mode
    >>> pipe.vae.enable_slicing()

    >>> # now let's upscale it
    >>> video = [Image.fromarray(frame).resize((1024, 576)) for frame in video_frames]

    >>> # and denoise it
    >>> video_frames = pipe(prompt, video=video, strength=0.6).frames
    >>> video_path = export_to_video(video_frames, output_video_path="./video_1024_spiderman.mp4")
    >>> video_path

####
disable_vae_slicing

< source >

( )

Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to computing decoding in one step.

####
disable_vae_tiling

< source >

( )

Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to computing decoding in one step.

####
enable_model_cpu_offload

< source >

( gpu_id = 0 )

Offload all models to CPU to reduce memory usage with a low impact on performance. Moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than using `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.

####
enable_vae_slicing

< source >

( )

Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.

####
enable_vae_tiling

< source >

( )

Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow processing larger images.

##
TextToVideoSDPipelineOutput

###
class diffusers.pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput

< source >

( frames: typing.Union[typing.List[numpy.ndarray], torch.FloatTensor] )

Parameters

- frames (`List[np.ndarray]` or `torch.FloatTensor`) — List of denoised frames (essentially images) as NumPy arrays of shape `(height, width, num_channels)` or as a `torch` tensor. The length of the list denotes the video length (the number of frames).

Output class for text-to-video pipelines.