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| import torch | |
| from transformers.tools.base import Tool, get_default_device | |
| from transformers.utils import is_accelerate_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.") | |
| 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 | |