VideoCrafter1: an open diffusion model
VideoCrafter1: an open diffusion model capable of generating high-quality videos
VideoCrafter1 is a high-quality video generation model developed by Tencent AI Lab. It has two models: text-to-video (T2V) and image-to-video (I2V).
Text to Video (T2V): Can generate video with movie quality and 1024×576 resolution.
Image to Video (I2V): Generates a video that strictly follows the content, structure, and style of a provided reference image.
Project address: https://t.co/XGNZJ7KizI
Paper: https://t.co/sbUs9Z3UbW
GitHub: https://t.co/whedZ00rMN
How the text-to-video (T2V) model works:
1. Infrastructure (SD 2.1): The model is built on SD 2.1, an advanced video generation network.
2. Temporal attention layer: In order to ensure that the generated videos are temporally coherent, the model adds a special “temporal attention layer” to the SD UNet network.
3. Video generation: Finally, the model can generate high-quality videos with a resolution of 1024×576 and a duration of 2 seconds.
Features and Benefits of text-to-video (T2V) model:
1. High resolution: Since the model can generate a resolution of 1024×576, this means that the output video quality will be very high.
2. Temporal consistency: The temporal attention layer ensures that the generated video is temporally coherent, thus avoiding any unnatural jumps or freezes.
How the image-to-video (I2V) model works:
The image-to-video (I2V) model is a key component in the VideoCrafter1 project, which is designed to generate videos that are highly consistent in content, structure, and style with a given reference image. This model accepts not only image input, but also text input, or both.
1. Image embedding: The model first uses the CLIP (Contrastive Language-Image Pretraining) algorithm to extract features from a given image. These features are called image embeddings.
2. Cross-attention mechanism: These image embeddings are injected into the SD UNet network through the cross-attention mechanism. This step allows the model to take into account the content and structure of the image when generating the video.
3. Video generation: After the embedding is injected, the model starts generating videos. Since the model has learned during training how to maintain content and structure consistent with the reference image, the generated video will strictly follow every aspect of the given image.
Features and Benefits of image-to-video (I2V) model:
- Content retention: This is the first open source I2V basic model that can strictly preserve the content of a given image while generating video.
2. Multi-input support: The model can accept images, text or a combination of both as input, providing more flexibility.
3. High-quality output: Because the model has been trained on a large number of data sets, it can generate high-quality videos.
After checking their discord channel, I found that the resolution of the generated video was indeed good, but the generated human video was severely deformed.
Animation, animation, and landscape-style videos are okay, but the characters are very hip-reaching and need to be improved.
If you are just interested, you can go to DC and check it out: https://t.co/7MT6AZhlXP
