README.md

EasyAnimateV5-12b-zh-InP-Reward-LoRAs

Introduction

We explore the Reward Backpropagation technique ^{1 2} to optimized the generated videos by EasyAnimateV5 for better alignment with human preferences. We provide pre-trained models (i.e. LoRAs) along with the training script. You can use these LoRAs to enhance the corresponding base model as a plug-in or train your own reward LoRA.

For more details, please refer to our GitHub repo.

Name	Base Model	Reward Model	Hugging Face	Description
EasyAnimateV5-12b-zh-InP-HPS2.1.safetensors	EasyAnimateV5-12b-zh-InP	HPS v2.1	🤗Link	Official HPS v2.1 reward LoRA (rank=128 and network_alpha=64) for EasyAnimateV5-12b-zh-InP. It is trained with a batch size of 8 for 2,500 steps.
EasyAnimateV5-7b-zh-InP-HPS2.1.safetensors	EasyAnimateV5-7b-zh-InP	HPS v2.1	🤗Link	Official HPS v2.1 reward LoRA (rank=128 and network_alpha=64) for EasyAnimateV5-7b-zh-InP. It is trained with a batch size of 8 for 3,500 steps.
EasyAnimateV5-12b-zh-InP-MPS.safetensors	EasyAnimateV5-12b-zh-InP	MPS	🤗Link	Official MPS reward LoRA (rank=128 and network_alpha=64) for EasyAnimateV5-12b-zh-InP. It is trained with a batch size of 8 for 2,500 steps.
EasyAnimateV5-7b-zh-InP-MPS.safetensors	EasyAnimateV5-7b-zh-InP	MPS	🤗Link	Official MPS reward LoRA (rank=128 and network_alpha=64) for EasyAnimateV5-7b-zh-InP. It is trained with a batch size of 8 for 2,000 steps.

Demo

EasyAnimateV5-12b-zh-InP

Prompt	EasyAnimateV5-12b-zh-InP	EasyAnimateV5-12b-zh-InP HPSv2.1 Reward LoRA	EasyAnimateV5-12b-zh-InP MPS Reward LoRA
Porcelain rabbit hopping by a golden cactus
Yellow rubber duck floating next to a blue bath towel
An elephant sprays water with its trunk, a lion sitting nearby
A fish swims gracefully in a tank as a horse gallops outside

EasyAnimateV5-7b-zh-InP

Prompt	EasyAnimateV5-7b-zh-InP	EasyAnimateV5-7b-zh-InP HPSv2.1 Reward LoRA	EasyAnimateV5-7b-zh-InP MPS Reward LoRA
Crystal cake shimmering beside a metal apple
Elderly artist with a white beard painting on a white canvas
Porcelain rabbit hopping by a golden cactus
Green parrot perching on a brown chair

The above test prompts are from T2V-CompBench. All videos are generated with lora weight 0.7.

Quick Start

We provide an example inference code to run EasyAnimateV5-12b-zh-InP with its HPS2.1 reward LoRA.

import torch
from diffusers import DDIMScheduler
from omegaconf import OmegaConf
from transformers import BertModel, BertTokenizer, T5EncoderModel, T5Tokenizer

from easyanimate.models import AutoencoderKLMagvit, EasyAnimateTransformer3DModel
from easyanimate.pipeline.pipeline_easyanimate_multi_text_encoder_inpaint import EasyAnimatePipeline_Multi_Text_Encoder_Inpaint
from easyanimate.utils.lora_utils import merge_lora
from easyanimate.utils.utils import get_image_to_video_latent, save_videos_grid
from easyanimate.utils.fp8_optimization import convert_weight_dtype_wrapper

# GPU memory mode, which can be choosen in [model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
GPU_memory_mode = "model_cpu_offload"
# Download from https://raw.githubusercontent.com/aigc-apps/EasyAnimate/refs/heads/main/config/easyanimate_video_v5_magvit_multi_text_encoder.yaml
config_path = "config/easyanimate_video_v5_magvit_multi_text_encoder.yaml"
model_path = "alibaba-pai/EasyAnimateV5-12b-zh-InP"
lora_path = "alibaba-pai/EasyAnimateV5-Reward-LoRAs/EasyAnimateV5-12b-zh-InP-HPS2.1.safetensors"
weight_dtype = torch.bfloat16
lora_weight = 0.7

prompt = "A panda eats bamboo while a monkey swings from branch to branch"
sample_size = [512, 512]
video_length = 49

config = OmegaConf.load(config_path)
transformer_additional_kwargs = OmegaConf.to_container(config['transformer_additional_kwargs'])
if weight_dtype == torch.float16:
    transformer_additional_kwargs["upcast_attention"] = True
transformer = EasyAnimateTransformer3DModel.from_pretrained_2d(
    model_path, 
    subfolder="transformer",
    transformer_additional_kwargs=transformer_additional_kwargs,
    torch_dtype=torch.float8_e4m3fn if GPU_memory_mode == "model_cpu_offload_and_qfloat8" else weight_dtype,
    low_cpu_mem_usage=True,
)
vae = AutoencoderKLMagvit.from_pretrained(
    model_path, subfolder="vae", vae_additional_kwargs=OmegaConf.to_container(config['vae_kwargs'])
).to(weight_dtype)
if config['vae_kwargs'].get('vae_type', 'AutoencoderKL') == 'AutoencoderKLMagvit' and weight_dtype == torch.float16:
    vae.upcast_vae = True

pipeline = EasyAnimatePipeline_Multi_Text_Encoder_Inpaint.from_pretrained(
    model_path,
    text_encoder=BertModel.from_pretrained(model_path, subfolder="text_encoder").to(weight_dtype),
    text_encoder_2=T5EncoderModel.from_pretrained(model_path, subfolder="text_encoder_2").to(weight_dtype),
    tokenizer=BertTokenizer.from_pretrained(model_path, subfolder="tokenizer"),
    tokenizer_2=T5Tokenizer.from_pretrained(model_path, subfolder="tokenizer_2"),
    vae=vae,
    transformer=transformer,
    scheduler=DDIMScheduler.from_pretrained(model_path, subfolder="scheduler"),
    torch_dtype=weight_dtype
)
if GPU_memory_mode == "sequential_cpu_offload":
    pipeline.enable_sequential_cpu_offload()
elif GPU_memory_mode == "model_cpu_offload_and_qfloat8":
    pipeline.enable_model_cpu_offload()
    convert_weight_dtype_wrapper(pipeline.transformer, weight_dtype)
else:
    pipeline.enable_model_cpu_offload()
pipeline = merge_lora(pipeline, lora_path, lora_weight)

generator = torch.Generator(device="cuda").manual_seed(42)
input_video, input_video_mask, _ = get_image_to_video_latent(None, None, video_length=video_length, sample_size=sample_size)
sample = pipeline(
    prompt, 
    video_length = video_length,
    negative_prompt = "bad detailed",
    height = sample_size[0],
    width = sample_size[1],
    generator = generator,
    guidance_scale = 7.0,
    num_inference_steps = 50,
    video = input_video,
    mask_video = input_video_mask,
).videos

save_videos_grid(sample, "samples/output.mp4", fps=8)

Limitations

1. We observe after training to a certain extent, the reward continues to increase, but the quality of the generated videos does not further improve. The model trickly learns some shortcuts (by adding artifacts in the background, i.e., adversarial patches) to increase the reward.
2. Currently, there is still a lack of suitable preference models for video generation. Directly using image preference models cannot evaluate preferences along the temporal dimension (such as dynamism and consistency). Further more, We find using image preference models leads to a decrease in the dynamism of generated videos. Although this can be mitigated by computing the reward using only the first frame of the decoded video, the impact still persists.

References

1. Clark, Kevin, et al. "Directly fine-tuning diffusion models on differentiable rewards.". In ICLR 2024.
2. Prabhudesai, Mihir, et al. "Aligning text-to-image diffusion models with reward backpropagation." arXiv preprint arXiv:2310.03739 (2023).