[FreeInit: Bridging Initialization Gap in Video Diffusion Models](https://arxiv.org/abs/2312.07537) by Tianxing Wu, Chenyang Si, Yuming Jiang, Ziqi Huang, Ziwei Liu.  
FreeInit is an effective method that improves temporal consistency and overall quality of videos generated using video-diffusion-models without any addition training. It can be applied to AnimateDiff, ModelScope, VideoCrafter and various other video generation models seamlessly at inference time, and works by iteratively refining the latent-initialization noise. More details can be found it the paper.  
The following example demonstrates the usage of FreeInit.  
```python
import torch
from diffusers import MotionAdapter, AnimateDiffPipeline, DDIMScheduler
from diffusers.utils import export_to_gif

adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2")
model_id = "SG161222/Realistic_Vision_V5.1_noVAE"
pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16).to("cuda")
pipe.scheduler = DDIMScheduler.from_pretrained(
model_id,
subfolder="scheduler",
beta_schedule="linear",
clip_sample=False,
timestep_spacing="linspace",
steps_offset=1
)

# enable memory savings
pipe.enable_vae_slicing()
pipe.enable_vae_tiling()

# enable FreeInit
# Refer to the enable_free_init documentation for a full list of configurable parameters
pipe.enable_free_init(method="butterworth", use_fast_sampling=True)

# run inference
output = pipe(
prompt="a panda playing a guitar, on a boat, in the ocean, high quality",
negative_prompt="bad quality, worse quality",
num_frames=16,
guidance_scale=7.5,
num_inference_steps=20,
generator=torch.Generator("cpu").manual_seed(666),
)

# disable FreeInit
pipe.disable_free_init()

frames = output.frames[0]
export_to_gif(frames, "animation.gif")
```  
<Tip warning={true}>  
FreeInit is not really free - the improved quality comes at the cost of extra computation. It requires sampling a few extra times depending on the `num_iters` parameter that is set when enabling it. Setting the `use_fast_sampling` parameter to `True` can improve the overall performance (at the cost of lower quality compared to when `use_fast_sampling=False` but still better results than vanilla video generation models).  
</Tip>  
<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>  
<table>
<tr>
<th align=center>Without FreeInit enabled</th>
<th align=center>With FreeInit enabled</th>
</tr>
<tr>
<td align=center>
panda playing a guitar
<br />
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-no-freeinit.gif"
alt="panda playing a guitar"
style="width: 300px;" />
</td>
<td align=center>
panda playing a guitar
<br/>
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-freeinit.gif"
alt="panda playing a guitar"
style="width: 300px;" />
</td>
</tr>
</table>
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/animatediff.md