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from PIL import Image |
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import torch |
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from transformers.agents.tools import Tool |
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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 DiffusionPipeline |
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if is_accelerate_available(): |
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from accelerate import PartialState |
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IMAGE_TRANSFORMATION_DESCRIPTION = ( |
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"This is a tool that transforms an image according to a prompt and returns the " |
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"modified image." |
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) |
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class ImageTransformationTool(Tool): |
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name = "image_transformation" |
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default_stable_diffusion_checkpoint = "timbrooks/instruct-pix2pix" |
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description = IMAGE_TRANSFORMATION_DESCRIPTION |
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inputs = { |
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'image': {"type": "image", "description": "the image to transform"}, |
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'prompt': {"type": "string", "description": "the prompt to use to change the image"} |
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} |
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output_type = "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.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 = PartialState().default_device |
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self.pipeline = DiffusionPipeline.from_pretrained(self.stable_diffusion) |
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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 forward(self, image, prompt): |
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if not self.is_initialized: |
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self.setup() |
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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=50, |
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).images[0] |