Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation by Jay Zhangjie Wu, Yixiao Ge, Xintao Wang, Stan Weixian Lei, Yuchao Gu, Yufei Shi, Wynne Hsu, Ying Shan, Xiaohu Qie, Mike Zheng Shou The abstract of the paper is the following:
To replicate the success of text-to-image (T2I) generation, recent works employ large-scale video datasets to train a text-to-video (T2V) generator. Despite their promising results, such paradigm is computationally expensive. In this work, we propose a new T2V generation setting—One-Shot Video Tuning, where only one text-video pair is presented. Our model is built on state-of-the-art T2I diffusion models pre-trained on massive image data. We make two key observations: 1) T2I models can generate still images that represent verb terms; 2) extending T2I models to generate multiple images concurrently exhibits surprisingly good content consistency. To further learn continuous motion, we introduce Tune-A-Video, which involves a tailored spatio-temporal attention mechanism and an efficient one-shot tuning strategy. At inference, we employ DDIM inversion to provide structure guidance for sampling. Extensive qualitative and numerical experiments demonstrate the remarkable ability of our method across various applications.
Resources:
Pipeline | Tasks | Demo |
---|---|---|
TuneAVideoPipeline | Text-to-Video Generation | 🤗 Spaces |
import torch
from diffusers import TuneAVideoPipeline, DDIMScheduler, UNet3DConditionModel
from diffusers.utils import export_to_video
from PIL import Image
# Use any pretrained Text2Image checkpoint based on stable diffusion
pretrained_model_path = "nitrosocke/mo-di-diffusion"
unet = UNet3DConditionModel.from_pretrained(
"Tune-A-Video-library/df-cpt-mo-di-bear-guitar", subfolder="unet", torch_dtype=torch.float16
).to("cuda")
pipe = TuneAVideoPipeline.from_pretrained(pretrained_model_path, unet=unet, torch_dtype=torch.float16).to("cuda")
prompt = "A princess playing a guitar, modern disney style"
generator = torch.Generator(device="cuda").manual_seed(42)
video_frames = pipe(prompt, video_length=3, generator=generator, num_inference_steps=50, output_type="np").frames
# Saving to gif.
pil_frames = [Image.fromarray(frame) for frame in video_frames]
duration = len(pil_frames) / 8
pil_frames[0].save(
"animation.gif",
save_all=True,
append_images=pil_frames[1:], # append rest of the images
duration=duration * 1000, # in milliseconds
loop=0,
)
# Saving to video
video_path = export_to_video(video_frames)
import torch
from diffusers import DiffusionPipeline, DDIMScheduler
from diffusers.utils import export_to_video
from PIL import Image
pipe = DiffusionPipeline.from_pretrained(
"Tune-A-Video-library/df-cpt-mo-di-bear-guitar", torch_dtype=torch.float16
).to("cuda")
prompt = "A princess playing a guitar, modern disney style"
generator = torch.Generator(device="cuda").manual_seed(42)
video_frames = pipe(prompt, video_length=3, generator=generator, num_inference_steps=50, output_type="np").frames
# Saving to gif.
pil_frames = [Image.fromarray(frame) for frame in video_frames]
duration = len(pil_frames) / 8
pil_frames[0].save(
"animation.gif",
save_all=True,
append_images=pil_frames[1:], # append rest of the images
duration=duration * 1000, # in milliseconds
loop=0,
)
# Saving to video
video_path = export_to_video(video_frames)
Here are some sample outputs:
![]() |
( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet3DConditionModel scheduler: typing.Union[diffusers.schedulers.scheduling_ddim.DDIMScheduler, diffusers.schedulers.scheduling_pndm.PNDMScheduler, diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler, diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler, diffusers.schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteScheduler, diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler] )
Parameters
CLIPTextModel
) —
Frozen text-encoder. Same as Stable Diffusion 2. CLIPTokenizer
) —
Tokenizer of class
CLIPTokenizer. unet
to denoise the encoded image latents. Can be one of
DDIMScheduler, LMSDiscreteScheduler, EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler, DPMSolverMultistepScheduler, or PNDMScheduler. Pipeline for text-to-video generation.
This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
( prompt: typing.Union[str, typing.List[str]] video_length: int = 8 height: typing.Optional[int] = None width: typing.Optional[int] = None num_inference_steps: int = 50 guidance_scale: float = 7.5 negative_prompt: typing.Union[str, typing.List[str], NoneType] = None num_videos_per_prompt: typing.Optional[int] = 1 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: typing.Optional[int] = 1 cross_attention_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None ) → ~pipelines.stable_diffusion.TuneAVideoPipelineOutput
or tuple
Parameters
str
or List[str]
, optional) —
The prompt or prompts to guide the video generation. If not defined, one has to pass prompt_embeds
.
instead. int
, optional, defaults to 8) —
The number of video frames that are generated. Defaults to 8 frames which at 8 frames per seconds
amounts to 1 second of video. int
, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) —
The height in pixels of the generated video. int
, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) —
The width in pixels of the generated video. 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. float
, optional, defaults to 7.5) —
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 videos that are closely linked to the text prompt
,
usually at the expense of lower video quality. 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. Ignored when not using guidance (i.e., ignored if guidance_scale
is
less than 1
). int
, optional, defaults to 1) —
Number of videos to be generated for each input prompt float
, optional, defaults to 0.0) —
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
schedulers.DDIMScheduler, will be ignored for others. torch.Generator
or List[torch.Generator]
, optional) —
One or a list of torch generator(s)
to make generation deterministic. 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 will ge generated by sampling using the supplied random generator
. Latents should be of shape
(batch_size, num_channel, num_frames, height, width)
. torch.FloatTensor
, 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. torch.FloatTensor
, optional) —
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt
weighting. If not provided, negative_prompt_embeds will be generated from negative_prompt
input
argument. str
, optional, defaults to "np"
) —
The output format of the generate video. Choose between torch.FloatTensor
or np.array
. bool
, optional, defaults to True
) —
Whether or not to return a TuneAVideoPipelineOutput
instead of a plain
tuple. Callable
, optional) —
A function that will be called every callback_steps
steps during inference. The function will be
called with the following arguments: callback(step: int, timestep: int, latents: torch.FloatTensor)
. int
, optional, defaults to 1) —
The frequency at which the callback
function will be called. If not specified, the callback will be
called at every step. dict
, optional) —
A kwargs dictionary that if specified is passed along to the AttentionProcessor
as defined under
self.processor
in
diffusers.cross_attention. Returns
~pipelines.stable_diffusion.TuneAVideoPipelineOutput
or tuple
~pipelines.stable_diffusion.TuneAVideoPipelineOutput
if return_dict
is True, otherwise a `tuple. When
returning a tuple, the first element is a list with the generated frames.
Function invoked when calling the pipeline for generation.
Examples:
>>> import torch
>>> from diffusers import UNet3DConditionModel, TuneAVideoPipeline, DDIMScheduler
>>> from diffusers.utils import export_to_video
>>> pretrained_model_path = "nitrosocke/mo-di-diffusion"
>>> scheduler = DDIMScheduler.from_pretrained(pretrained_model_path, subfolder="scheduler")
>>> unet = UNet3DConditionModel.from_pretrained(
... "Tune-A-Video-library/df-cpt-mo-di-bear-guitar", subfolder="unet", torch_dtype=torch.float16
... ).to("cuda")
>>> pipe = TuneAVideoPipeline.from_pretrained(
... pretrained_model_path, unet=unet, scheduler=scheduler, torch_dtype=torch.float16
... ).to("cuda")
>>> prompt = "a magical princess is playing guitar, modern disney style"
>>> video_frames = pipe(
... prompt,
... video_length=4,
... height=256,
... width=256,
... num_inference_steps=38,
... guidance_scale=12.5,
... output_type="np",
... ).frames
>>> video_frames = pipe(prompt).frames
>>> video_path = export_to_video(video_frames)
>>> video_path
Disable sliced VAE decoding. If enable_vae_slicing
was previously enabled, this method will go back to
computing decoding in one step.
Disable tiled VAE decoding. If enable_vae_tiling
was previously enabled, this method will go back to
computing decoding in one step.
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 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.
( prompt device num_videos_per_prompt do_classifier_free_guidance negative_prompt = None prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None lora_scale: typing.Optional[float] = None )
Parameters
str
or List[str]
, optional) —
prompt to be encoded
device — (torch.device
):
torch device int
) —
number of images that should be generated per prompt bool
) —
whether to use classifier free guidance or not 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
). torch.FloatTensor
, 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. torch.FloatTensor
, optional) —
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt
weighting. If not provided, negative_prompt_embeds will be generated from negative_prompt
input
argument. float
, optional) —
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. Encodes the prompt into text encoder hidden states.