Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators is by Levon Khachatryan, Andranik Movsisyan, Vahram Tadevosyan, Roberto Henschel, Zhangyang Wang, Shant Navasardyan, Humphrey Shi.
Text2Video-Zero enables zero-shot video generation using either:
Results are temporally consistent and closely follow the guidance and textual prompts.
The abstract from the paper is:
Recent text-to-video generation approaches rely on computationally heavy training and require large-scale video datasets. In this paper, we introduce a new task of zero-shot text-to-video generation and propose a low-cost approach (without any training or optimization) by leveraging the power of existing text-to-image synthesis methods (e.g., Stable Diffusion), making them suitable for the video domain. Our key modifications include (i) enriching the latent codes of the generated frames with motion dynamics to keep the global scene and the background time consistent; and (ii) reprogramming frame-level self-attention using a new cross-frame attention of each frame on the first frame, to preserve the context, appearance, and identity of the foreground object. Experiments show that this leads to low overhead, yet high-quality and remarkably consistent video generation. Moreover, our approach is not limited to text-to-video synthesis but is also applicable to other tasks such as conditional and content-specialized video generation, and Video Instruct-Pix2Pix, i.e., instruction-guided video editing. As experiments show, our method performs comparably or sometimes better than recent approaches, despite not being trained on additional video data.
You can find additional information about Text-to-Video Zero on the project page, paper, and original codebase.
To generate a video from prompt, run the following python command
import torch
import imageio
from diffusers import TextToVideoZeroPipeline
model_id = "runwayml/stable-diffusion-v1-5"
pipe = TextToVideoZeroPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
prompt = "A panda is playing guitar on times square"
result = pipe(prompt=prompt).images
result = [(r * 255).astype("uint8") for r in result]
imageio.mimsave("video.mp4", result, fps=4)
You can change these parameters in the pipeline call:
motion_field_strength_x
and motion_field_strength_y
. Default: motion_field_strength_x=12
, motion_field_strength_y=12
T
and T'
(see the paper, Sect. 3.3.1)t0
and t1
in the range {0, ..., num_inference_steps}
. Default: t0=45
, t1=48
video_length
, the number of frames video_length to be generated. Default: video_length=8
We an also generate longer videos by doing the processing in a chunk-by-chunk manner:
import torch
import imageio
from diffusers import TextToVideoZeroPipeline
import numpy as np
model_id = "runwayml/stable-diffusion-v1-5"
pipe = TextToVideoZeroPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
seed = 0
video_length = 8
chunk_size = 4
prompt = "A panda is playing guitar on times square"
# Generate the video chunk-by-chunk
result = []
chunk_ids = np.arange(0, video_length, chunk_size - 1)
generator = torch.Generator(device="cuda")
for i in range(len(chunk_ids)):
print(f"Processing chunk {i + 1} / {len(chunk_ids)}")
ch_start = chunk_ids[i]
ch_end = video_length if i == len(chunk_ids) - 1 else chunk_ids[i + 1]
# Attach the first frame for Cross Frame Attention
frame_ids = [0] + list(range(ch_start, ch_end))
# Fix the seed for the temporal consistency
generator.manual_seed(seed)
output = pipe(prompt=prompt, video_length=len(frame_ids), generator=generator, frame_ids=frame_ids)
result.append(output.images[1:])
# Concatenate chunks and save
result = np.concatenate(result)
result = [(r * 255).astype("uint8") for r in result]
imageio.mimsave("video.mp4", result, fps=4)
To generate a video from prompt with additional pose control
Download a demo video
from huggingface_hub import hf_hub_download
filename = "__assets__/poses_skeleton_gifs/dance1_corr.mp4"
repo_id = "PAIR/Text2Video-Zero"
video_path = hf_hub_download(repo_type="space", repo_id=repo_id, filename=filename)
Read video containing extracted pose images
from PIL import Image
import imageio
reader = imageio.get_reader(video_path, "ffmpeg")
frame_count = 8
pose_images = [Image.fromarray(reader.get_data(i)) for i in range(frame_count)]
To extract pose from actual video, read ControlNet documentation.
Run StableDiffusionControlNetPipeline
with our custom attention processor
import torch
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor
model_id = "runwayml/stable-diffusion-v1-5"
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose", torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
model_id, controlnet=controlnet, torch_dtype=torch.float16
).to("cuda")
# Set the attention processor
pipe.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))
pipe.controlnet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))
# fix latents for all frames
latents = torch.randn((1, 4, 64, 64), device="cuda", dtype=torch.float16).repeat(len(pose_images), 1, 1, 1)
prompt = "Darth Vader dancing in a desert"
result = pipe(prompt=[prompt] * len(pose_images), image=pose_images, latents=latents).images
imageio.mimsave("video.mp4", result, fps=4)
To generate a video from prompt with additional pose control, follow the steps described above for pose-guided generation using Canny edge ControlNet model.
To perform text-guided video editing (with InstructPix2Pix):
Download a demo video
from huggingface_hub import hf_hub_download
filename = "__assets__/pix2pix video/camel.mp4"
repo_id = "PAIR/Text2Video-Zero"
video_path = hf_hub_download(repo_type="space", repo_id=repo_id, filename=filename)
Read video from path
from PIL import Image
import imageio
reader = imageio.get_reader(video_path, "ffmpeg")
frame_count = 8
video = [Image.fromarray(reader.get_data(i)) for i in range(frame_count)]
Run StableDiffusionInstructPix2PixPipeline
with our custom attention processor
import torch
from diffusers import StableDiffusionInstructPix2PixPipeline
from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor
model_id = "timbrooks/instruct-pix2pix"
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
pipe.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=3))
prompt = "make it Van Gogh Starry Night style"
result = pipe(prompt=[prompt] * len(video), image=video).images
imageio.mimsave("edited_video.mp4", result, fps=4)
Methods Text-To-Video, Text-To-Video with Pose Control and Text-To-Video with Edge Control can run with custom DreamBooth models, as shown below for Canny edge ControlNet model and Avatar style DreamBooth model
Download a demo video
from huggingface_hub import hf_hub_download
filename = "__assets__/canny_videos_mp4/girl_turning.mp4"
repo_id = "PAIR/Text2Video-Zero"
video_path = hf_hub_download(repo_type="space", repo_id=repo_id, filename=filename)
Read video from path
from PIL import Image
import imageio
reader = imageio.get_reader(video_path, "ffmpeg")
frame_count = 8
canny_edges = [Image.fromarray(reader.get_data(i)) for i in range(frame_count)]
Run StableDiffusionControlNetPipeline
with custom trained DreamBooth model
import torch
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor
# set model id to custom model
model_id = "PAIR/text2video-zero-controlnet-canny-avatar"
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
model_id, controlnet=controlnet, torch_dtype=torch.float16
).to("cuda")
# Set the attention processor
pipe.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))
pipe.controlnet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))
# fix latents for all frames
latents = torch.randn((1, 4, 64, 64), device="cuda", dtype=torch.float16).repeat(len(canny_edges), 1, 1, 1)
prompt = "oil painting of a beautiful girl avatar style"
result = pipe(prompt=[prompt] * len(canny_edges), image=canny_edges, latents=latents).images
imageio.mimsave("video.mp4", result, fps=4)
You can filter out some available DreamBooth-trained models with this link.
( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel scheduler: KarrasDiffusionSchedulers safety_checker: StableDiffusionSafetyChecker feature_extractor: CLIPImageProcessor requires_safety_checker: bool = True )
Parameters
CLIPTextModel
) —
Frozen text-encoder (clip-vit-large-patch14). CLIPTokenizer
) —
A CLIPTokenizer to tokenize text. unet
to denoise the encoded image latents. Can be one of
DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler. StableDiffusionSafetyChecker
) —
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the model card for more details
about a model’s potential harms. CLIPImageProcessor
) —
A CLIPImageProcessor
to extract features from generated images; used as inputs to the safety_checker
. Pipeline for zero-shot text-to-video generation using Stable Diffusion.
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.).
( prompt: typing.Union[str, typing.List[str]] video_length: typing.Optional[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 motion_field_strength_x: float = 12 motion_field_strength_y: float = 12 output_type: typing.Optional[str] = 'tensor' return_dict: bool = True callback: typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None callback_steps: typing.Optional[int] = 1 t0: int = 44 t1: int = 47 frame_ids: typing.Optional[typing.List[int]] = None ) → TextToVideoPipelineOutput
Parameters
str
or List[str]
, optional) —
The prompt or prompts to guide image generation. If not defined, you need to pass prompt_embeds
. int
, optional, defaults to 8) —
The number of generated video frames. int
, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor
) —
The height in pixels of the generated image. int
, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor
) —
The width in pixels of the generated image. int
, optional, defaults to 50) —
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. 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
. 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
). int
, optional, defaults to 1) —
The number of videos to generate per prompt. 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. torch.Generator
or List[torch.Generator]
, optional) —
A torch.Generator
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 is generated by sampling using the supplied random generator
. str
, optional, defaults to "numpy"
) —
The output format of the generated video. Choose between "latent"
and "numpy"
. bool
, optional, defaults to True
) —
Whether or not to return a
TextToVideoPipelineOutput instead of
a plain tuple. 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)
. int
, optional, defaults to 1) —
The frequency at which the callback
function is called. If not specified, the callback is called at
every step. float
, optional, defaults to 12) —
Strength of motion in generated video along x-axis. See the paper,
Sect. 3.3.1. float
, optional, defaults to 12) —
Strength of motion in generated video along y-axis. See the paper,
Sect. 3.3.1. int
, optional, defaults to 44) —
Timestep t0. Should be in the range [0, num_inference_steps - 1]. See the
paper, Sect. 3.3.1. int
, optional, defaults to 47) —
Timestep t0. Should be in the range [t0 + 1, num_inference_steps - 1]. See the
paper, Sect. 3.3.1. List[int]
, optional) —
Indexes of the frames that are being generated. This is used when generating longer videos
chunk-by-chunk. Returns
The output contains a ndarray
of the generated video, when output_type
!= "latent"
, otherwise a
latent code of generated videos and a list of bool
s indicating whether the corresponding generated
video contains “not-safe-for-work” (nsfw) content..
The call function to the pipeline for generation.
( latents timesteps prompt_embeds guidance_scale callback callback_steps num_warmup_steps extra_step_kwargs cross_attention_kwargs = None ) → latents
Parameters
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)
. int
, optional, defaults to 1) —
The frequency at which the callback
function is called. If not specified, the callback is called at
every step.
extra_step_kwargs —
Extra_step_kwargs.
cross_attention_kwargs —
A kwargs dictionary that if specified is passed along to the AttentionProcessor
as defined in
self.processor
.
num_warmup_steps —
number of warmup steps. Returns
latents
Latents of backward process output at time timesteps[-1].
Perform backward process given list of time steps.
( x_t0 t0 t1 generator ) → x_t1
Parameters
torch.Generator
or List[torch.Generator]
, optional) —
A torch.Generator
to make
generation deterministic. Returns
x_t1
Forward process applied to x_t0 from time t0 to t1.
Perform DDPM forward process from time t0 to t1. This is the same as adding noise with corresponding variance.
( images: typing.Union[typing.List[PIL.Image.Image], numpy.ndarray] nsfw_content_detected: typing.Optional[typing.List[bool]] )
Parameters
[List[PIL.Image.Image]
, np.ndarray
]) —
List of denoised PIL images of length batch_size
or NumPy array of shape (batch_size, height, width, num_channels)
. [List[bool]]
) —
List indicating whether the corresponding generated image contains “not-safe-for-work” (nsfw) content or
None
if safety checking could not be performed. Output class for zero-shot text-to-video pipeline.