Text2Video-Zero
译者:片刻小哥哥
项目地址:https://huggingface.apachecn.org/docs/diffusers/api/pipelines/text_to_video_zero
原始地址:https://huggingface.co/docs/diffusers/api/pipelines/text_to_video_zero
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:
- A textual prompt
- A prompt combined with guidance from poses or edges
- Video Instruct-Pix2Pix (instruction-guided video editing)
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 .
Usage example
Text-To-Video
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 (see the
paper
, Sect. 3.3.1):
motion_field_strength_x
andmotion_field_strength_y
. Default:motion_field_strength_x=12
,motion_field_strength_y=12
T
andT'
(see the paper , Sect. 3.3.1)t0
andt1
in the range{0,..., num_inference_steps}
. Default:t0=45
,t1=48
- Video length:
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)
Text-To-Video with Pose Control
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
.
3. 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)
Text-To-Video with Edge Control
To generate a video from prompt with additional pose control, follow the steps described above for pose-guided generation using Canny edge ControlNet model .
Video Instruct-Pix2Pix
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)
DreamBooth specialization
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 .
TextToVideoZeroPipeline
class
diffusers.
TextToVideoZeroPipeline
[<
source
](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py#L274)
(
vae
: AutoencoderKL
text_encoder
: CLIPTextModel
tokenizer
: CLIPTokenizer
unet
: UNet2DConditionModel
scheduler
: KarrasDiffusionSchedulers
safety_checker
: StableDiffusionSafetyChecker
feature_extractor
: CLIPImageProcessor
requires_safety_checker
: bool = True
)
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 ( UNet2DConditionModel ) — 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 . - safety_checker
(
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. - feature_extractor
(
CLIPImageProcessor
) — ACLIPImageProcessor
to extract features from generated images; used as inputs to thesafety_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.).
__call__
[<
source
](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py#L421)
(
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
)
→
export const metadata = 'undefined';
Parameters
- prompt
(
str
orList[str]
, optional ) — The prompt or prompts to guide image generation. If not defined, you need to passprompt_embeds
. - video_length
(
int
, optional , defaults to 8) — The number of generated video frames. - height
(
int
, optional , defaults toself.unet.config.sample_size * self.vae_scale_factor
) — The height in pixels of the generated image. - width
(
int
, optional , defaults toself.unet.config.sample_size * self.vae_scale_factor
) — The width in pixels of the generated image. - num_inference_steps
(
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. - guidance_scale
(
float
, optional , defaults to 7.5) — A higher guidance scale value encourages the model to generate images closely linked to the textprompt
at the expense of lower image quality. Guidance scale is enabled whenguidance_scale > 1
. - negative_prompt
(
str
orList[str]
, optional ) — The prompt or prompts to guide what to not include in video generation. If not defined, you need to passnegative_prompt_embeds
instead. Ignored when not using guidance (guidance_scale < 1
). - num_videos_per_prompt
(
int
, optional , defaults to 1) — The number of videos 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
orList[torch.Generator]
, optional ) — Atorch.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 randomgenerator
. - output_type
(
str
, optional , defaults to"numpy"
) — The output format of the generated video. Choose between"latent"
and"numpy"
. - return_dict
(
bool
, optional , defaults toTrue
) — Whether or not to return a TextToVideoPipelineOutput instead of a plain tuple. - callback
(
Callable
, optional ) — A function that calls everycallback_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 thecallback
function is called. If not specified, the callback is called at every step. - motion_field_strength_x
(
float
, optional , defaults to 12) — Strength of motion in generated video along x-axis. See the paper , Sect. 3.3.1. - motion_field_strength_y
(
float
, optional , defaults to 12) — Strength of motion in generated video along y-axis. See the paper , Sect. 3.3.1. - t0
(
int
, optional , defaults to 44) — Timestep t0. Should be in the range [0, num_inference_steps - 1]. See the paper , Sect. 3.3.1. - t1
(
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. - frame_ids
(
List[int]
, optional ) — Indexes of the frames that are being generated. This is used when generating longer videos chunk-by-chunk.
Returns
export const metadata = 'undefined';
export const metadata = 'undefined';
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.
backward_loop
[<
source
](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py#L346)
(
latents
timesteps
prompt_embeds
guidance_scale
callback
callback_steps
num_warmup_steps
extra_step_kwargs
cross_attention_kwargs
= None
)
→
export const metadata = 'undefined';
latents
Parameters
- callback
(
Callable
, optional ) — A function that calls everycallback_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 thecallback
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 theAttentionProcessor
as defined inself.processor
. num_warmup_steps — number of warmup steps.
Returns
export const metadata = 'undefined';
latents
export const metadata = 'undefined';
Latents of backward process output at time timesteps[-1].
Perform backward process given list of time steps.
forward_loop
[<
source
](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py#L322)
(
x_t0
t0
t1
generator
)
→
export const metadata = 'undefined';
x_t1
Parameters
- generator
(
torch.Generator
orList[torch.Generator]
, optional ) — Atorch.Generator
to make generation deterministic.
Returns
export const metadata = 'undefined';
x_t1
export const metadata = 'undefined';
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.
TextToVideoPipelineOutput
class
diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.
TextToVideoPipelineOutput
[<
source
](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py#L174)
(
images
: typing.Union[typing.List[PIL.Image.Image], numpy.ndarray]
nsfw_content_detected
: typing.Optional[typing.List[bool]]
)
Parameters
- images
(
[List[PIL.Image.Image]
,np.ndarray
]) — List of denoised PIL images of lengthbatch_size
or NumPy array of shape(batch_size, height, width, num_channels)
. - nsfw_content_detected
(
[List[bool]]
) — List indicating whether the corresponding generated image contains “not-safe-for-work” (nsfw) content orNone
if safety checking could not be performed.
Output class for zero-shot text-to-video pipeline.