| from transformers.tools.base import Tool, get_default_device |
| from transformers.utils import is_accelerate_available |
| import torch |
|
|
| from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler |
|
|
|
|
| TEXT_TO_IMAGE_DESCRIPTION = ( |
| "This is a tool that creates an image according to a prompt, which is a text description. It takes an input named `prompt` which " |
| "contains the image description and outputs an image." |
| ) |
|
|
|
|
| class TextToImageTool(Tool): |
| default_checkpoint = "runwayml/stable-diffusion-v1-5" |
| description = TEXT_TO_IMAGE_DESCRIPTION |
| inputs = ['text'] |
| outputs = ['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 = get_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] |
|
|
|
|
