2307.04725 AnimateDiff Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning

AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning §

Authors: Yuwei Guo, Ceyuan Yang, Anyi Rao, Yaohui Wang, Yu Qiao, Dahua Lin, Bo Dai

Abstract: With the advance of text-to-image models (e.g., Stable Diffusion) and corresponding personalization techniques such as DreamBooth and LoRA, everyone can manifest their imagination into high-quality images at an affordable cost. Subsequently, there is a great demand for image animation techniques to further combine generated static images with motion dynamics. In this report, we propose a practical framework to animate most of the existing personalized text-to-image models once and for all, saving efforts in model-specific tuning. At the core of the proposed framework is to insert a newly initialized motion modeling module into the frozen text-to-image model and train it on video clips to distill reasonable motion priors. Once trained, by simply injecting this motion modeling module, all personalized versions derived from the same base T2I readily become text-driven models that produce diverse and personalized animated images. We conduct our evaluation on several public representative personalized text-to-image models across anime pictures and realistic photographs, and demonstrate that our proposed framework helps these models generate temporally smooth animation clips while preserving the domain and diversity of their outputs. Code and pre-trained weights will be publicly available at animatediff.github.io .

What, Why and How §

Here is a summary of the key points from the paper:

What:

• The paper proposes AnimateDiff, a framework to enable personalized text-to-image models to generate animated images without model-specific tuning.

Why:

• Existing approaches to animate personalized text-to-image models require collecting personalized videos and model-specific tuning, which is challenging.
• The goal is to animate personalized models once and for all without compromising their original quality and diversity.

How:

• A motion modeling module is trained on large-scale video datasets to learn general motion priors.
• This module can then be inserted into any personalized model derived from the same base model to generate smooth and personalized animations.
• The module uses temporal transformers and is trained while keeping base model weights frozen.
• Experiments show it can animate diverse personalized models like anime, cartoons and real images without tuning.

In summary, AnimateDiff aims to animate personalized text-to-image models without model-specific tuning by transferring motion priors learned on videos using a plug-and-play motion modeling module. This provides a simple yet effective solution for personalized animation generation.

Main Contributions §

Here are the main contributions of this paper:

• Proposes a new task of animating personalized text-to-image models without model-specific tuning. This enables bringing static personalized image models to life.

• Introduces AnimateDiff, a practical framework to animate personalized models by transferring motion priors from videos using a plug-and-play motion modeling module.

• Designs a motion modeling module using temporal transformers that is trained on large-scale video datasets while keeping base model frozen.

• Demonstrates AnimateDiff can animate diverse personalized models like anime, cartoons, realistic images without compromising quality or diversity.

• Provides extensive experiments on multiple personalized models verifying the effectiveness and generalization ability of the proposed method.

• Establishes a strong baseline for personalized animation generation that only requires personalizing an image model, saving efforts of collecting personalized videos.

To summarize, the key contributions are:

1. New task formulation for personalized animation without tuning
2. AnimateDiff framework for animating personalized models
3. Design of plug-and-play motion modeling module
4. Effectiveness demonstrated across diverse personalized models
5. Strong generalized baseline for personalized animation

Method Section §

Here is a summary of the method section from the paper:

The method section first introduces preliminaries about text-to-image (T2I) models like Stable Diffusion and personalization techniques like DreamBooth and LoRA.

It then formulates the problem of personalized animation where the goal is to animate a personalized T2I model without specific tuning or collecting personalized videos.

The key idea is to train a separate motion modeling module on large-scale video datasets to learn generalizable motion priors. This avoids model-specific tuning.

The motion modeling module uses temporal transformers and is trained by inserting it into the base T2I model and keeping base weights frozen. It is trained on video clips to reconstruct noise sequences.

To use this module, any personalized model derived from the base model is inflated to make it compatible. The trained motion module is then simply inserted into this personalized model.

During inference, the personalized model with the inserted motion module takes a text prompt and generates an animation clip with proper motions learned from videos while retaining personalized domain knowledge.

In summary, the method introduces a plug-and-play motion module to transfer motion priors from videos to animate personalized models without specific tuning through inflation and insertion.

High-Level Pseudo Code §

Here is the high-level pseudo code for the method proposed in the paper:

# Train motion modeling module
 
base_model = StableDiffusion() 
 
motion_module = TemporalTransformer()
 
large_video_dataset = WebVid10M()
 
Insert motion_module into base_model
 
Train motion_module on large_video_dataset
    Optimize motion_module parameters only 
    Keep base_model parameters frozen
 
# Use for personalized animation
 
personalized_model = FineTuneBaseModel(base_model) 
 
Inflate personalized_model to be compatible with videos
 
Insert pre-trained motion_module into inflated personalized_model
 
for text_prompt in prompts:
   animation = personalized_model(text_prompt)

In summary, the key steps are:

1. Insert motion module into base model
2. Train module on videos while freezing base weights
3. Inflate personalized model for compatibility
4. Insert pre-trained motion module
5. Generate animations using personalized model

This illustrates how the motion module is trained once on videos and then inserted into any personalized model derived from the base to enable personalized animation without specific tuning.

Detailed Pseudo Code §

Here is a more detailed pseudo code to implement the method proposed in the paper:

# Motion modeling module
class MotionModule(nn.Module):
  def __init__(self):
    super().__init__()
    self.temp_trans = TemporalTransformer() 
    # Temporal transformer 
 
  def forward(self, x):
    b, c, f, h, w = x.shape 
    x = x.permute(0,2,1,3,4) # (B,F,C,H,W)
    x = x.reshape(b*f, c, h, w)
 
    feats = []
    for feat in x.split(b):
      feat = self.temp_trans(feat) 
      feats.append(feat)
    
    x = torch.cat(feats, dim=0)
    x = x.reshape(b, f, c, h, w).permute(0,2,1,3,4)
    return x
 
# Insert into model  
model = StableDiffusion()
model.motion_module = MotionModule() # Insert
 
# Train module
opt = torch.optim.Adam(model.motion_module.parameters(), lr=1e-4)
 
for x in dataloader: # Video batches
  x = model.encode(x) # Encode
  x_noise = add_noise(x) 
  
  pred = model(x_noise)
  loss = MSELoss(pred, noise) 
  
  opt.zero_grad()
  loss.backward()
  opt.step()
 
# Use for animation
personalized_model = FineTuneModel(model) 
 
texts = ["Prompt 1", "Prompt 2"]
for text in texts:
  z = personalized_model.encode(text)
  
  animation = personalized_model.decode(z) 

This shows the key steps including:

• Motion module definition with temporal transformer
• Insertion into base model
• Training on video batches while freezing base weights
• Using personalized model inflated with module to generate animations

Let me know if you would like me to explain or expand on any part of this detailed pseudo code!