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import os
from videogen_hub import MODEL_PATH
class ShowOne():
def __init__(self):
"""
Initialize the Pipeline, which download all necessary models.
"""
from videogen_hub.pipelines.show_1.run_inference import ShowOnePipeline
from huggingface_hub import snapshot_download
base_path = snapshot_download(
repo_id="showlab/show-1-base",
local_dir=os.path.join(MODEL_PATH, "showlab", "show-1-base"),
local_dir_use_symlinks = False
)
interp_path = snapshot_download(
repo_id="showlab/show-1-interpolation",
local_dir=os.path.join(MODEL_PATH, "showlab", "show-1-interpolation"),
)
deepfloyd_path = snapshot_download(
repo_id="DeepFloyd/IF-II-L-v1.0",
local_dir=os.path.join(MODEL_PATH, "DeepFloyd/IF-II-L-v1.0"),
)
sr1_path = snapshot_download(
repo_id="showlab/show-1-sr1",
local_dir=os.path.join(MODEL_PATH, "showlab", "show-1-sr1"),
)
sr2_path = snapshot_download(
repo_id="showlab/show-1-sr2",
local_dir=os.path.join(MODEL_PATH, "showlab", "show-1-sr2"),
)
self.pipeline = ShowOnePipeline(base_path, interp_path, deepfloyd_path, sr1_path, sr2_path)
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 a textual prompt. The output is a tensor representing the video.
Since the initial_num_frames is set to be 8 as shown in paper in the pipeline,
we need the (number of frames - 1) divisible by 7 to manage interpolation.
Args:
prompt (str, optional): The text prompt that guides the video generation. If not specified, the video generation will rely solely on the input image. Defaults to None.
size (list, optional): Specifies the resolution of the output 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 number of frames per second in the generated video. This determines how smooth the video appears. Defaults to 8.
seed (int, optional): A seed value for random number generation, ensuring reproducibility of the video generation process. Defaults to 42.
Returns:
torch.Tensor: A tensor representing the generated video, structured as (time, channel, height, width).
"""
num_frames = fps * seconds
assert (num_frames - 1) % 7 == 0
scaling_factor = (num_frames - 1) // 7
video = self.pipeline.inference(prompt=prompt,
negative_prompt="",
output_size=size,
initial_num_frames=8,
scaling_factor=scaling_factor,
seed=seed)
return video
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