samsara-ku
commented
3 months ago @johndpope
Hi! I'm looking around your nice project nowadays so that I have a question for your project, especially about the diffusion_transformer
In the paper, they apply transformer architecture for their diffusion process and cite some papers like DiT.
In particular, we apply a transformer architecture [55, 36, 50] for our sequence generation task.So the point is like this: Is it okay to implement diffusion_transformer architecture w/o details of DiT like adaLN-zero? I think the authors (maybe) implement their own tricks kind of that for HQ qualitative results.
I just want to know your opinion about this issue.
johndpope
Expanding the provided code to fully recreate the VASA-1 system as described in the research paper would require a significant amount of additional code and architectural changes. Here's a high-level outline of the key components and changes needed:
Expressive and Disentangled Face Latent Space: Implement the 3D-aided face representation using a canonical 3D appearance volume, identity code, 3D head pose, and facial dynamics code. Design and implement the losses for learning the disentangled latent space, including the pairwise head pose and facial dynamics transfer loss and the face identity similarity loss for cross-identity motion transfer. Train the face encoder and decoder on the VoxCeleb2 dataset to learn the disentangled latent space. Holistic Facial Dynamics Generation with Diffusion Transformer: Implement the diffusion transformer model for generating holistic facial dynamics and head motion sequences conditioned on audio and other control signals. Modify the training pipeline to use the denoising score matching objective for the diffusion model. Implement the conditioning signals, including the audio features, main eye gaze direction, head-to-camera distance, and emotion offset. Apply classifier-free guidance during inference for controllable generation. Talking Face Video Generation: Modify the generator architecture to take the generated motion latent codes and the appearance and identity features from the face encoder as input. Implement the sliding-window approach for generating long video sequences efficiently. Training and Evaluation: Prepare the training datasets, including the preprocessed VoxCeleb2 dataset and the high-resolution talk video dataset. Implement the training loop with the appropriate losses and optimization techniques. Evaluate the generated videos using metrics like audio-lip synchronization (SyncNet), audio-pose alignment (CAPP score), pose variation intensity, and Fréchet Video Distance (FVD). Real-time Optimization: Optimize the model architectures and inference pipeline to achieve real-time generation of high-resolution videos (e.g., 512x512 at 40 FPS). Implement techniques like model compression, efficient architectures, and parallel processing to reduce computational overhead. Additional Features: Incorporate support for controllable generation using the optional conditioning signals like main gaze direction, head distance, and emotion offset. Implement the capability to handle out-of-distribution images and audio inputs, such as artistic photos, singing audio clips, and non-English speech. Please note that implementing all these components and changes would require a significant amount of code, as well as access to the necessary datasets and computational resources. The provided code serves as a starting point, but substantial modifications and additions would be needed to fully recreate the VASA-1 system.
It's important to refer to the original research paper for more detailed information on the architectures, training procedures, and specific implementation details to ensure an accurate reproduction of the VASA-1 model.