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_opencv_available, is_vision_available, ) if is_vision_available(): from PIL import Image if is_diffusers_available(): from diffusers import ControlNetModel, StableDiffusionControlNetPipeline, UniPCMultistepScheduler if is_opencv_available(): import cv2 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 = "runwayml/stable-diffusion-v1-5" default_controlnet_checkpoint = "lllyasviel/sd-controlnet-canny" description = IMAGE_TRANSFORMATION_DESCRIPTION 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.") if not is_opencv_available(): raise ImportError("opencv should be installed in order to use the StableDiffusionTool.") super().__init__() if controlnet is None: controlnet = self.default_controlnet_checkpoint self.controlnet_checkpoint = controlnet if stable_diffusion is None: stable_diffusion = self.default_stable_diffusion_checkpoint self.stable_diffusion_checkpoint = stable_diffusion self.device = device self.hub_kwargs = hub_kwargs def setup(self): if self.device is None: self.device = get_default_device() self.controlnet = ControlNetModel.from_pretrained(self.controlnet_checkpoint, torch_dtype=torch.float16) self.pipeline = StableDiffusionControlNetPipeline.from_pretrained( self.stable_diffusion_checkpoint, controlnet=self.controlnet, torch_dtype=torch.float16 ) self.pipeline.scheduler = UniPCMultistepScheduler.from_config(self.pipeline.scheduler.config) self.pipeline.enable_model_cpu_offload() self.is_initialized = True def __call__(self, image, prompt): if not self.is_initialized: self.setup() initial_prompt = "super-hero character, best quality, extremely detailed" prompt = initial_prompt + prompt low_threshold = 100 high_threshold = 200 image = np.array(image) image = cv2.Canny(image, low_threshold, high_threshold) image = image[:, :, None] image = np.concatenate([image, image, image], axis=2) canny_image = Image.fromarray(image) generator = torch.Generator(device="cpu").manual_seed(2) return self.pipeline( prompt, canny_image, negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality", num_inference_steps=20, generator=generator, ).images[0]