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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 | |
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.") | |
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) | |
self.pipeline = StableDiffusionControlNetPipeline.from_pretrained( | |
self.stable_diffusion_checkpoint, controlnet=self.controlnet | |
) | |
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] | |