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import os
from huggingface_hub import hf_hub_download
from videogen_hub import MODEL_PATH
class StreamingT2V:
def __init__(self, device="cuda"):
"""
Initializes the StreamingT2V model.
Args:
device (str, optional): The device to run the model on. Defaults to "cuda".
"""
from videogen_hub.pipelines.streamingt2v.streamingt2v_pipeline import pipeline
# https://huggingface.co/spaces/PAIR/StreamingT2V/resolve/main/t2v_enhanced/checkpoints/streaming_t2v.ckpt?download=true
model_url = "https://huggingface.co/spaces/PAIR/StreamingT2V/resolve/main/t2v_enhanced/checkpoints/streaming_t2v.ckpt?download=true"
# Download the file
ckpt_file_streaming_t2v = hf_hub_download(repo_id="PAIR/StreamingT2V",
filename="streaming_t2v.ckpt",
local_dir=os.path.join(MODEL_PATH, "streamingtv2"))
self.pipeline = pipeline
def infer_one_video(
self,
prompt: str = None,
size: list = [320, 512],
seconds: int = 2,
fps: int = 8,
seed: int = 42,
):
"""
Generates a single video based on the provided prompt and parameters.
Args:
prompt (str, optional): The text prompt to generate the video from. Defaults to None.
size (list, optional): The size of the video as [height, width]. Defaults to [320, 512].
seconds (int, optional): The duration of the video in seconds. Defaults to 2.
fps (int, optional): The frames per second of the video. Defaults to 8.
seed (int, optional): The seed for random number generation. Defaults to 42.
Returns:
torch.Tensor: The generated video as a tensor.
"""
return self.pipeline(prompt, size, seconds, fps, seed)
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