from PIL import Image from transformers.tools.base import Tool from transformers.utils import is_accelerate_available import torch from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler if is_accelerate_available(): from accelerate import PartialState TEXT_TO_IMAGE_DESCRIPTION = ( "This is a tool that creates an image according to a prompt, which is a text description." ) class TextToImageTool(Tool): default_checkpoint = "runwayml/stable-diffusion-v1-5" description = TEXT_TO_IMAGE_DESCRIPTION name = "image_generator" inputs = {"prompt": {"type": str, "description": "contains the image description and outputs an image"}} output_type = Image.Image 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 = PartialState().default_device self.pipeline = DiffusionPipeline.from_pretrained(self.default_checkpoint) self.pipeline.scheduler = DPMSolverMultistepScheduler.from_config(self.pipeline.scheduler.config) self.pipeline.to(self.device) if self.device.type == "cuda": self.pipeline.to(torch_dtype=torch.float16) self.is_initialized = True def __call__(self, prompt): if not self.is_initialized: self.setup() negative_prompt = "low quality, bad quality, deformed, low resolution" added_prompt = " , highest quality, highly realistic, very high resolution" return self.pipeline(prompt + added_prompt, negative_prompt=negative_prompt, num_inference_steps=25).images[0]