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import numpy as np |
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import torch |
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import cv2 |
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from transformers.tools.base import Tool, get_default_device |
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from transformers.utils import ( |
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is_accelerate_available, |
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is_vision_available, |
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) |
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from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler |
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if is_vision_available(): |
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from PIL import Image |
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IMAGE_TRANSFORMATION_DESCRIPTION = ( |
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"This is a tool that transforms an image with ControlNet according to a prompt. It takes two inputs: `image`, which should be " |
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"the image to transform, and `prompt`, which should be the prompt to use to change it. It returns the " |
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"modified image." |
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) |
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class ControlNetTransformationTool(Tool): |
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default_stable_diffusion_checkpoint = "runwayml/stable-diffusion-v1-5" |
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default_controlnet_checkpoint = "lllyasviel/control_v11p_sd15_canny" |
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description = IMAGE_TRANSFORMATION_DESCRIPTION |
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name = "image_transformer" |
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inputs = ['image', 'text'] |
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outputs = ['image'] |
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def __init__(self, device=None, controlnet=None, stable_diffusion=None, **hub_kwargs) -> None: |
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if not is_accelerate_available(): |
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raise ImportError("Accelerate should be installed in order to use tools.") |
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if not is_vision_available(): |
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raise ImportError("Pillow should be installed in order to use the StableDiffusionTool.") |
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super().__init__() |
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self.stable_diffusion = self.default_stable_diffusion_checkpoint |
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self.controlnet = self.default_controlnet_checkpoint |
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self.device = device |
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self.hub_kwargs = hub_kwargs |
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def setup(self): |
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if self.device is None: |
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self.device = get_default_device() |
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controlnet = ControlNetModel.from_pretrained(self.controlnet) |
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self.pipeline = StableDiffusionControlNetPipeline.from_pretrained(self.stable_diffusion, controlnet=controlnet) |
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self.pipeline.scheduler = UniPCMultistepScheduler.from_config(self.pipeline.scheduler.config) |
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self.pipeline.to(self.device) |
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if self.device.type == "cuda": |
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self.pipeline.to(torch_dtype=torch.float16) |
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self.is_initialized = True |
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def __call__(self, image, prompt): |
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if not self.is_initialized: |
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self.setup() |
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image = np.array(image) |
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image = cv2.Canny(image, 100, 200) |
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image = image[:, :, None] |
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image = np.concatenate([image, image, image], axis=2) |
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image = Image.fromarray(image) |
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negative_prompt = "low quality, bad quality, deformed, low resolution" |
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added_prompt = " , highest quality, highly realistic, very high resolution" |
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return self.pipeline( |
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prompt + added_prompt, |
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image, |
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negative_prompt=negative_prompt, |
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num_inference_steps=30, |
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).images[0] |
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