

# Fork of [stabilityai/stable-diffusion-2-inpainting](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting)

> Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
> For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion with 🧨Diffusers blog](https://huggingface.co/blog/stable_diffusion).

For more information about the model, license and limitations check the original model card at [stabilityai/stable-diffusion-2-inpainting](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting).

---

This repository implements a custom `handler` task for `text-guided-to-image-inpainting` for 🤗 Inference Endpoints. The code for the customized pipeline is in the [handler.py](https://huggingface.co/philschmid/stable-diffusion-2-inpainting-endpoint/blob/main/handler.py).

There is also a [notebook](https://huggingface.co/philschmid/stable-diffusion-2-inpainting-endpoint/blob/main/create_handler.ipynb) included, on how to create the `handler.py`

![thubmnail](Stable%20Diffusion%20Inference%20endpoints%20-%20inpainting.png)


### expected Request payload

```json
{
    "inputs": "A prompt used for image generation",
    "image" : "iVBORw0KGgoAAAANSUhEUgAAAgAAAAIACAIAAAB7GkOtAAAABGdBTUEAALGPC",
    "mask_image": "iVBORw0KGgoAAAANSUhEUgAAAgAAAAIACAIAAAB7GkOtAAAABGdBTUEAALGPC",
}
```

below is an example on how to run a request using Python and `requests`.

## Run Request 
```python
import json
from typing import List
import requests as r
import base64
from PIL import Image
from io import BytesIO

ENDPOINT_URL = ""
HF_TOKEN = ""

# helper image utils
def encode_image(image_path):
  with open(image_path, "rb") as i:
    b64 = base64.b64encode(i.read())
  return b64.decode("utf-8")


def predict(prompt, image, mask_image):
    image = encode_image(image)
    mask_image = encode_image(mask_image)

    # prepare sample payload
    request = {"inputs": prompt, "image": image, "mask_image": mask_image}
    # headers
    headers = {
        "Authorization": f"Bearer {HF_TOKEN}",
        "Content-Type": "application/json",
        "Accept": "image/png" # important to get an image back
    }

    response = r.post(ENDPOINT_URL, headers=headers, json=payload)
    img = Image.open(BytesIO(response.content))
    return img

prediction = predict(
    prompt="Face of a bengal cat, high resolution, sitting on a park bench",
    image="dog.png",
    mask_image="mask_dog.png"
)
```
expected output

![sample](result.png)

{"license": "openrail++", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-guided-to-image-inpainting", "endpoints-template"], "thumbnail": "https://huggingface.co/philschmid/stable-diffusion-2-inpainting-endpoint/resolve/main/Stable%20Diffusion%20Inference%20endpoints%20-%20inpainting.png", "inference": true, "duplicated_from": "philschmid/stable-diffusion-2-inpainting-endpoint"}
diffusers stable-diffusion stable-diffusion-diffusers text-guided-to-image-inpainting endpoints-template license:openrail++ endpoints_compatible diffusers:StableDiffusionInpaintPipeline region:us
TAGS
#diffusers #stable-diffusion #stable-diffusion-diffusers #text-guided-to-image-inpainting #endpoints-template #license-openrail++ #endpoints_compatible #diffusers-StableDiffusionInpaintPipeline #region-us 



# Fork of stabilityai/stable-diffusion-2-inpainting

> Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
> For more information about how Stable Diffusion functions, please have a look at 's Stable Diffusion with Diffusers blog.

For more information about the model, license and limitations check the original model card at stabilityai/stable-diffusion-2-inpainting.

---

This repository implements a custom 'handler' task for 'text-guided-to-image-inpainting' for  Inference Endpoints. The code for the customized pipeline is in the URL.

There is also a notebook included, on how to create the 'URL'

!thubmnail


### expected Request payload



below is an example on how to run a request using Python and 'requests'.

## Run Request 

expected output

!sample

# Fork of stabilityai/stable-diffusion-2-inpainting

> Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
> For more information about how Stable Diffusion functions, please have a look at 's Stable Diffusion with Diffusers blog.

For more information about the model, license and limitations check the original model card at stabilityai/stable-diffusion-2-inpainting.

---

This repository implements a custom 'handler' task for 'text-guided-to-image-inpainting' for  Inference Endpoints. The code for the customized pipeline is in the URL.

There is also a notebook included, on how to create the 'URL'

!thubmnail ### expected Request payload



below is an example on how to run a request using Python and 'requests'. ## Run Request 

expected output

!sample
TAGS
#diffusers #stable-diffusion #stable-diffusion-diffusers #text-guided-to-image-inpainting #endpoints-template #license-openrail++ #endpoints_compatible #diffusers-StableDiffusionInpaintPipeline #region-us 
 # Fork of stabilityai/stable-diffusion-2-inpainting

> Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
> For more information about how Stable Diffusion functions, please have a look at 's Stable Diffusion with Diffusers blog.

For more information about the model, license and limitations check the original model card at stabilityai/stable-diffusion-2-inpainting.

---

This repository implements a custom 'handler' task for 'text-guided-to-image-inpainting' for  Inference Endpoints. The code for the customized pipeline is in the URL.

There is also a notebook included, on how to create the 'URL'

!thubmnail ### expected Request payload



below is an example on how to run a request using Python and 'requests'. ## Run Request 

expected output

!sample
TAGS
#diffusers #stable-diffusion #stable-diffusion-diffusers #text-guided-to-image-inpainting #endpoints-template #license-openrail++ #endpoints_compatible #diffusers-StableDiffusionInpaintPipeline #region-us 
# Fork of stabilityai/stable-diffusion-2-inpainting

> Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
> For more information about how Stable Diffusion functions, please have a look at 's Stable Diffusion with Diffusers blog.

For more information about the model, license and limitations check the original model card at stabilityai/stable-diffusion-2-inpainting.

---

This repository implements a custom 'handler' task for 'text-guided-to-image-inpainting' for  Inference Endpoints. The code for the customized pipeline is in the URL.

There is also a notebook included, on how to create the 'URL'

!thubmnail### expected Request payload



below is an example on how to run a request using Python and 'requests'.## Run Request 

expected output

!sample