| from transformers.agents.tools 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." | |
| ) | |
| class TextToImageTool(Tool): | |
| default_checkpoint = "runwayml/stable-diffusion-v1-5" | |
| description = TEXT_TO_IMAGE_DESCRIPTION | |
| name = "image_generator" | |
| inputs = {"prompt": {"type": "text", "description": "the image description"}} | |
| output_type = "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] | |