add: files.

app.py

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+from transformers.tools.base import launch_gradio_demo
+from text_to_image_inpainting import InpatingTool
+
+launch_gradio_demo(InpatingTool)

requirements.txt

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+transformers
+diffusers
+accelerate
+torch

text_to_image_inpainting.py

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+from transformers.tools.base import Tool, get_default_device
+from transformers.utils import is_accelerate_available
+import torch
+
+from diffusers import StableDiffusionInpaintPipeline
+
+
+INPAINTING_DESCRIPTION = (
+    "This is a tool that inpaints some parts of an image StableDiffusionInpaintPipeline according to a prompt."
+    " It takes three inputs: `image`, which should be the original image which will be inpainted,"
+    " `mask_image`, which should be used to determine which parts of the original image"
+    " (stored in the `image` variable) should be inpainted,"
+    " and `prompt`, which should be the prompt to use to guide the inpainting process. It returns the"
+    " inpainted image."
+)
+
+
+class InpatingTool(Tool):
+    default_checkpoint = "stabilityai/stable-diffusion-2-inpainting"
+    description = INPAINTING_DESCRIPTION
+    inputs = ['text']
+    outputs = ['image']
+
+    def __init__(self, device=None, **hub_kwargs) -> None:
+        if not is_accelerate_available():
+            raise ImportError("Accelerate should be installed in order to use tools.")
+
+        super().__init__()
+
+        self.device = device
+        self.pipeline = None
+        self.hub_kwargs = hub_kwargs
+
+    def setup(self):
+        if self.device is None:
+            self.device = get_default_device()
+
+        self.pipeline = StableDiffusionInpaintPipeline.from_pretrained(self.default_checkpoint)
+        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, mask_image,  prompt):
+        if not self.is_initialized:
+            self.setup()
+
+        negative_prompt = "low quality, bad quality, deformed, low resolution"
+        added_prompt = " , highest quality, highly realistic, very high resolution"
+
+        return self.pipeline(
+            prompt=prompt + added_prompt,
+            negative_prompt=negative_prompt,
+            image=image,
+            mask_image=mask_image
+        ).images[0]

tool_config.json

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+{
+    "tool_class": "text_to_image_inpainting.InpatingTool",
+    "description": "This is a tool that inpaints some parts of an image StableDiffusionInpaintPipeline according to a prompt. It takes three inputs: `image`, which should be the original image which will be inpainted, `mask_image`, which should be used to determine which parts of the original image (stored in the `image` variable) should be inpainted, and `prompt`, which should be the prompt to use to guide the inpainting process. It returns the inpainted image.",
+    "name": "image_inpainter"
+  }