TempoFunk/makeavid-sd-jax

Make-A-Video SD JAX Model Card

A latent diffusion model for text-to-video synthesis.

Try it with an interactive demo on HuggingFace spaces.

Training code, PyTorch and FLAX implementation are available here: https://github.com/lopho/makeavid-sd-tpu

This model extends an inpainting latent-diffusion image generation model (Stable Diffusion v1.5 Inpaint) with temporal convolution and temporal self-attention ported from Make-A-Video PyTorch

It has then been fine tuned for ~150k steps on a dataset of 10,000 videos themed around dance. Then for an additional ~50k steps with extra data of generic videos mixed into the original set.

This model used weights pretrained by lxj616 on 286 timelapse video clips for initialization.

Table of Contents

• Model Details
• Uses
• Limitations
• Training
  • Training Data
  • Training Process
  • Hyper parameters
• Acknowledgements
• Citation

Model Details

• Developed by: Lopho, Chavinlo
• Model type: Diffusion based text-to-video generation model
• Language(s): English
• License: (pending) GNU Affero General Public License 3.0
• Further resources: Model implementation & training code, Weights & Biases training statistics

Uses

• Understanding limitations and biases of generative video models
• Development of educational or creative tools
• Artistic usage
• What ever you want

Limitations

• Limited knowledge of temporal concepts not seen during training (see linked datasets)
• Emerging flashing lights, most likely due to training on dance videos, which include many scenes with bright, neon and flashing lights
• The model has only been trained with English captions and will not perform as well in other languages

Training

Training Data

• S(mall)dance: 10,000 video-caption pairs of dancing videos (as encoded image latents, text embeddings and metadata).
• small: 7,000 video-caption pairs of general videos (as encoded image latents, text embeddings and metadata).

Training Procedure

• From each video sample a random range of 24 frames is selected
• Each video latent is encoded into latent representations of the shape 4 x 24 x H/8 x W/8
• The latent of the first frame from each video is repeated along the frame dimension as additional guidance (referred to as hint image)
• Hint latent and video latent are stacked to produce a shape of 8 x 24 x H/8 x W/8
• The last input channel is preserved for masking purposes (not used during training, set to zero)
• Text prompts are encoded by the CLIP text encoder
• Video latents with added noise and clip encoded text prompts are fed into the UNet to predict the added noise
• Loss is the reconstruction objective between the added noise and the predicted noise via mean squared error (mse/l2)

Hyperparameters

• Batch size: 1 x 4
• Image size: 512 x 512
• Frame count: 24
• Optimizer: AdamW (beta_1 = 0.9, beta_2 = 0.999, weight decay = 0.02)
• Schedule:
  • 2 x 10 epochs: LR warmup for 1 epochs then held constant at 5e-5 (10,000 samples per ep)
  • 2 x 20 epochs: LR warmup for 1 epochs then held constant at 5e-5 (10,000 samples per ep)
  • 1 x 9 epochs: LR warmup for 1 epoch to 5e-5 then cosine annealing to 1e-8
  • Additional data mixed in, see Trainig Data
  • 1 x 5 epochs: LR warmup for 0.5 epochs to 2.5e-5 then constant (17,000 samples per ep)
  • 1 x 5 epochs: LR warmup for 0.5 epochs to 5e-6 then cosine annealing to 2.5e-6 (17,000 samples per ep)
  • some restarts were required due to NaNs appearing in the gradient (see training logs)
• Total update steps: ~200,000
• Hardware: TPUv4-8 (provided by Google Cloud for the HuggingFace JAX/Diffusers Sprint Event)

Trainig statistics are available at Weights and Biases.

Acknowledgements

• CompVis for Latent Diffusion Models + Stable Diffusion
• Meta AIs Make-A-Video for the research of applying pseudo 3D convolution and attention to existing image models
• Phil Wang for the torch implementation of Make-A-Video Pseudo3D convolution and attention
• lxj616 for initial proof of feasibility of LDM + Make-A-Video

Citation

@misc{TempoFunk2023,
      author = {Lopho, Carlos Chavez},
      title = {TempoFunk: Extending latent diffusion image models to Video},
      url = {https://github.com/lopho/makeavid-sd-tpu},
      month = {5},
      year = {2023}
}

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This model card was written by: Lopho, Chavinlo, Julian Herrera and is based on the DALL-E Mini model card.