|
|
| import numpy as np |
| import torch |
|
|
| from transformers.tools.base import Tool, get_default_device |
| from transformers.utils import ( |
| is_accelerate_available, |
| is_diffusers_available, |
| is_vision_available, |
| ) |
|
|
|
|
| if is_vision_available(): |
| from PIL import Image |
|
|
| if is_diffusers_available(): |
| from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler |
|
|
|
|
| IMAGE_TRANSFORMATION_DESCRIPTION = ( |
| "This is a tool that transforms an image according to a prompt. It takes two inputs: `image`, which should be " |
| "the image to transform, and `prompt`, which should be the prompt to use to change it. It returns the " |
| "modified image." |
| ) |
|
|
|
|
| class ImageTransformationTool(Tool): |
| default_stable_diffusion_checkpoint = "timbrooks/instruct-pix2pix" |
| description = IMAGE_TRANSFORMATION_DESCRIPTION |
| inputs = ['image', 'text'] |
| outputs = ['image'] |
|
|
| def __init__(self, device=None, controlnet=None, stable_diffusion=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.") |
| if not is_vision_available(): |
| raise ImportError("Pillow should be installed in order to use the StableDiffusionTool.") |
|
|
| super().__init__() |
|
|
| self.stable_diffusion = self.default_stable_diffusion_checkpoint |
|
|
| self.device = device |
| self.hub_kwargs = hub_kwargs |
|
|
| def setup(self): |
| if self.device is None: |
| self.device = get_default_device() |
|
|
| self.pipeline = DiffusionPipeline.from_pretrained(self.stable_diffusion) |
| 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, image, 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, |
| image, |
| negative_prompt=negative_prompt, |
| num_inference_steps=40, |
| ).images[0] |
|
|
