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>
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md