Text2Video-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:

1. A textual prompt
2. A prompt combined with guidance from poses or edges
3. 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 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:
  • 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

1. Download a demo video

   Copied

   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)

2. Read video containing extracted pose images

   Copied

   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

   Copied

   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):

1. Download a demo video

   Copied

   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)

2. Read video from path

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   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)]

3. Run StableDiffusionInstructPix2PixPipeline with our custom attention processor

   Copied

   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

1. Download a demo video

   Copied

   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)

2. Read video from path

   Copied

   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)]

3. Run StableDiffusionControlNetPipeline with custom trained DreamBooth model

   Copied

   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

TextToVideoPipelineOutput