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| from PIL import Image | |
| import numpy as np | |
| import torch | |
| from transformers.tools.base import Tool, get_default_device | |
| from transformers.utils import is_accelerate_available | |
| from diffusers import DiffusionPipeline | |
| IMAGE_UPSCALING_DESCRIPTION = ( | |
| "This is a tool that upscales an image. It takes one input: `image`, which should be " | |
| "the image to upscale. It returns the upscaled image." | |
| ) | |
| class ImageUpscalingTool(Tool): | |
| default_stable_diffusion_checkpoint = "stabilityai/sd-x2-latent-upscaler" | |
| description = IMAGE_UPSCALING_DESCRIPTION | |
| name = "image_upscaler" | |
| inputs = ['image'] | |
| 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.") | |
| 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.to(self.device) | |
| if self.device.type == "cuda": | |
| self.pipeline.to(torch_dtype=torch.float16) | |
| self.is_initialized = True | |
| def __call__(self, image): | |
| if not self.is_initialized: | |
| self.setup() | |
| print(f"Received image type: {type(image)}") | |
| # Convert NumPy array to PIL Image | |
| if isinstance(image, np.ndarray): | |
| image = Image.fromarray((image * 255).astype(np.uint8)) | |
| print(f"Converted image type: {type(image)}") | |
| # After converting the image | |
| image.show() | |
| return self.pipeline( | |
| image=image, | |
| prompt="", | |
| num_inference_steps=30, | |
| guidance_scale=0, | |
| ).images[0] | |