TL;DR: We propose StyleCrafter, a generic method that enhances pre-trained T2V models with style control, supporting Style-Guided Text-to-Image Generation and Style-Guided Text-to-Video Generation.

Style-guided text-to-video results. Resolution: 320 x 512; Frames: 16. (Compressed)

Style-guided text-to-image results. Resolution: 512 x 512. (Compressed)

• [2023.12.08]: 🔥🔥 Release the Huggingface online demo.
• [2023.12.05]: 🔥🔥 Release the code and checkpoint.
• [2023.11.30]: 🔥🔥 Release the project page.

• Remove Video Watermark(due to trained on WebVid10M).

It takes approximately 5 seconds to generate a 512×512 image and 85 seconds to generate a 320×512 video with 16 frames using a single NVIDIA A100 (40G) GPU. A GPU with at least 16G GPU memory is required to perform the inference process.

conda create -n stylecrafter python=3.8.5
conda activate stylecrafter
pip install -r requirements.txt

1. Download all checkpoints according to the instructions
2. Run the commands in terminal.

# style-guided text-to-image generation
sh scripts/run_infer_image.sh

# style-guided text-to-video generation
sh scripts/run_infer_video.sh

3. (Optional) Infernce on your own data according to the instructions

VideoCrafter1: Framework for high-quality text-to-video generation.

ScaleCrafter: Tuning-free method for high-resolution image/video generation.

TaleCrafter: An interactive story visualization tool that supports multiple characters.

LongerCrafter: Tuning-free method for longer high-quality video generation.

DynamiCrafter Animate open-domain still images to high-quality videos.

We develop this repository for RESEARCH purposes, so it can only be used for personal/research/non-commercial purposes.

We would like to thank AK(@_akhaliq) for the help of setting up online demo.

If your have any comments or questions, feel free to contact [email protected]