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# EasyAnimate
[EasyAnimate](https://github.com/aigc-apps/EasyAnimate) by Alibaba PAI.
The description from it's GitHub page:
*EasyAnimate is a pipeline based on the transformer architecture, designed for generating AI images and videos, and for training baseline models and Lora models for Diffusion Transformer. We support direct prediction from pre-trained EasyAnimate models, allowing for the generation of videos with various resolutions, approximately 6 seconds in length, at 8fps (EasyAnimateV5.1, 1 to 49 frames). Additionally, users can train their own baseline and Lora models for specific style transformations.*
This pipeline was contributed by [bubbliiiing](https://github.com/bubbliiiing). The original codebase can be found [here](https://huggingface.co/alibaba-pai). The original weights can be found under [hf.co/alibaba-pai](https://huggingface.co/alibaba-pai).
There are two official EasyAnimate checkpoints for text-to-video and video-to-video.
| checkpoints | recommended inference dtype |
|:---:|:---:|
| [`alibaba-pai/EasyAnimateV5.1-12b-zh`](https://huggingface.co/alibaba-pai/EasyAnimateV5.1-12b-zh) | torch.float16 |
| [`alibaba-pai/EasyAnimateV5.1-12b-zh-InP`](https://huggingface.co/alibaba-pai/EasyAnimateV5.1-12b-zh-InP) | torch.float16 |
There is one official EasyAnimate checkpoints available for image-to-video and video-to-video.
| checkpoints | recommended inference dtype |
|:---:|:---:|
| [`alibaba-pai/EasyAnimateV5.1-12b-zh-InP`](https://huggingface.co/alibaba-pai/EasyAnimateV5.1-12b-zh-InP) | torch.float16 |
There are two official EasyAnimate checkpoints available for control-to-video.
| checkpoints | recommended inference dtype |
|:---:|:---:|
| [`alibaba-pai/EasyAnimateV5.1-12b-zh-Control`](https://huggingface.co/alibaba-pai/EasyAnimateV5.1-12b-zh-Control) | torch.float16 |
| [`alibaba-pai/EasyAnimateV5.1-12b-zh-Control-Camera`](https://huggingface.co/alibaba-pai/EasyAnimateV5.1-12b-zh-Control-Camera) | torch.float16 |
For the EasyAnimateV5.1 series:
- Text-to-video (T2V) and Image-to-video (I2V) works for multiple resolutions. The width and height can vary from 256 to 1024.
- Both T2V and I2V models support generation with 1~49 frames and work best at this value. Exporting videos at 8 FPS is recommended.
## Quantization
Quantization helps reduce the memory requirements of very large models by storing model weights in a lower precision data type. However, quantization may have varying impact on video quality depending on the video model.
Refer to the [Quantization](../../quantization/overview) overview to learn more about supported quantization backends and selecting a quantization backend that supports your use case. The example below demonstrates how to load a quantized [`EasyAnimatePipeline`] for inference with bitsandbytes.
```py
import torch
from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig, EasyAnimateTransformer3DModel, EasyAnimatePipeline
from diffusers.utils import export_to_video
quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True)
transformer_8bit = EasyAnimateTransformer3DModel.from_pretrained(
"alibaba-pai/EasyAnimateV5.1-12b-zh",
subfolder="transformer",
quantization_config=quant_config,
torch_dtype=torch.float16,
)
pipeline = EasyAnimatePipeline.from_pretrained(
"alibaba-pai/EasyAnimateV5.1-12b-zh",
transformer=transformer_8bit,
torch_dtype=torch.float16,
device_map="balanced",
)
prompt = "A cat walks on the grass, realistic style."
negative_prompt = "bad detailed"
video = pipeline(prompt=prompt, negative_prompt=negative_prompt, num_frames=49, num_inference_steps=30).frames[0]
export_to_video(video, "cat.mp4", fps=8)
```
## EasyAnimatePipeline
[[autodoc]] EasyAnimatePipeline
- all
- __call__
## EasyAnimatePipelineOutput
[[autodoc]] pipelines.easyanimate.pipeline_output.EasyAnimatePipelineOutput
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