import torch from transformers.tools.base import Tool, get_default_device from transformers.utils import is_accelerate_available, is_diffusers_available if is_diffusers_available(): from diffusers import DiffusionPipeline TEXT_TO_VIDEO_DESCRIPTION = ( "This is a tool that creates a video according to a text description. It takes an input named `prompt` which " "contains the image description, as well as an optional input `seconds` which will be the duration of the video. " "The default is of two seconds. The tool outputs a video object." ) class TextToVideoTool(Tool): default_checkpoint = "damo-vilab/text-to-video-ms-1.7b" description = TEXT_TO_VIDEO_DESCRIPTION inputs = ['text'] outputs = ['video'] def __init__(self, device=None, **hub_kwargs) -> None: if not is_accelerate_available(): raise ImportError("Accelerate should be installed in order to use tools.") if not is_diffusers_available(): raise ImportError("Diffusers should be installed in order to use the StableDiffusionTool.") super().__init__() self.device = device self.pipeline = None self.hub_kwargs = hub_kwargs def setup(self): if self.device is None: self.device = get_default_device() self.pipeline = DiffusionPipeline.from_pretrained( self.default_checkpoint, variant="fp16" ) self.pipeline.to(self.device) self.is_initialized = True def __call__(self, prompt, seconds=2): if not self.is_initialized: self.setup() return self.pipeline(prompt, num_frames=8 * seconds).frames