T2V-Turbo
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With the style of low-poly game art, A majestic, white horse gallops gracefully across a moonlit beach. | medium shot of Christine, a beautiful 25-year-old brunette resembling Selena Gomez, anxiously looking up as she walks down a New York street, cinematic style | a cartoon pig playing his guitar, Andrew Warhol style |
a dog wearing vr goggles on a boat | Pikachu snowboarding | a girl floating underwater |
This repository contains unet_lora.pt
that can turn VideoCrafter2 into our T2V-Turbo (VC2). Our T2V-Turbo (VC2) can achieve both fast and high-quality T2V generation. On VBench, the 4-step generation from our T2V-Turbo (VC2) even outperform proprietary systems, including Gen-2 and Pika. Please refer to our GitHub repo for detailed instructions.
This checkpoint is obtained by merging the UNet LoRA weight to the UNet of VideoCrafter2. Therefore, the checkpoint here is also under the apache-2.0 license.
You need to first clone our GitHub repo. Here are the codes to load the checkpoint.
from utils.common_utils import load_model_checkpoint
from utils.utils import instantiate_from_config
config = OmegaConf.load("configs/inference_t2v_512_v2.0.yaml")
model_config = config.pop("model", OmegaConf.create())
pretrained_t2v = instantiate_from_config(model_config)
unet_config = model_config["params"]["unet_config"]
unet_config["params"]["time_cond_proj_dim"] = 256
unet = instantiate_from_config(unet_config)
pretrained_t2v.model.diffusion_model = unet
pretrained_t2v = load_model_checkpoint(pretrained_t2v, "checkpoints/t2v_turbo_vc2.pt")
Our model is meant for research purposes.