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# Text2Video-Zero

<div class="flex flex-wrap space-x-1">
  <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
</div>

[Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators](https://huggingface.co/papers/2303.13439) is by Levon Khachatryan, Andranik Movsisyan, Vahram Tadevosyan, Roberto Henschel, [Zhangyang Wang](https://www.ece.utexas.edu/people/faculty/atlas-wang), Shant Navasardyan, [Humphrey Shi](https://www.humphreyshi.com).

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.

![teaser-img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/t2v_zero_teaser.png)

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 Text2Video-Zero on the [project page](https://text2video-zero.github.io/), [paper](https://arxiv.org/abs/2303.13439), and [original codebase](https://github.com/Picsart-AI-Research/Text2Video-Zero).

## Usage example

### Text-To-Video

To generate a video from prompt, run the following Python code:
```python
import torch
from diffusers import TextToVideoZeroPipeline
import imageio

model_id = "stable-diffusion-v1-5/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](https://arxiv.org/abs/2303.13439), 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](https://arxiv.org/abs/2303.13439), 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 can also generate longer videos by doing the processing in a chunk-by-chunk manner:
```python
import torch
from diffusers import TextToVideoZeroPipeline
import numpy as np

model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
pipe = TextToVideoZeroPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
seed = 0
video_length = 24  #24 ÷ 4fps = 6 seconds
chunk_size = 8
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)
```


- #### SDXL Support
In order to use the SDXL model when generating a video from prompt, use the `TextToVideoZeroSDXLPipeline` pipeline:

```python
import torch
from diffusers import TextToVideoZeroSDXLPipeline

model_id = "stabilityai/stable-diffusion-xl-base-1.0"
pipe = TextToVideoZeroSDXLPipeline.from_pretrained(
    model_id, torch_dtype=torch.float16, variant="fp16", use_safetensors=True
).to("cuda")
```

### Text-To-Video with Pose Control
To generate a video from prompt with additional pose control

1. Download a demo video

    ```python
    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
    ```python
    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](controlnet).

3. Run `StableDiffusionControlNetPipeline` with our custom attention processor

    ```python
    import torch
    from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
    from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor

    model_id = "stable-diffusion-v1-5/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)
    ```
- #### SDXL Support

	Since our attention processor also works with SDXL, it can be utilized to generate a video from prompt using ControlNet models powered by SDXL:
	```python
	import torch
	from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel
	from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor

	controlnet_model_id = 'thibaud/controlnet-openpose-sdxl-1.0'
	model_id = 'stabilityai/stable-diffusion-xl-base-1.0'

	controlnet = ControlNetModel.from_pretrained(controlnet_model_id, 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, 128, 128), 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 Canny edge control, follow the same steps described above for pose-guided generation using [Canny edge ControlNet model](https://huggingface.co/lllyasviel/sd-controlnet-canny).


### Video Instruct-Pix2Pix

To perform text-guided video editing (with [InstructPix2Pix](pix2pix)):

1. Download a demo video

    ```python
    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
    ```python
    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
    ```python
    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](../../training/dreambooth) models, as shown below for
[Canny edge ControlNet model](https://huggingface.co/lllyasviel/sd-controlnet-canny) and
[Avatar style DreamBooth](https://huggingface.co/PAIR/text2video-zero-controlnet-canny-avatar) model:

1. Download a demo video

    ```python
    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
    ```python
    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
    ```python
    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](https://huggingface.co/models?search=dreambooth).

<Tip>

Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.

</Tip>

## TextToVideoZeroPipeline
[[autodoc]] TextToVideoZeroPipeline
	- all
	- __call__

## TextToVideoZeroSDXLPipeline
[[autodoc]] TextToVideoZeroSDXLPipeline
	- all
	- __call__

## TextToVideoPipelineOutput
[[autodoc]] pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.TextToVideoPipelineOutput
