initial commit

.gitignore

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+# PhpStorm / IDEA
+.idea
+

README.md

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+---
+tags:
+ - vision
+ - image-to-image
+ - endpoints-template
+ - super-resolution
+inference: false
+pipeline_tag: image-to-image
+library_name: generic
+---
+
+# [DAT-x2](https://huggingface.co/OzzyGT/DAT_X2) for a `image-to-image` Inference endpoint.
+
+> Inspired by https://huggingface.co/sergeipetrov/dat-x2-ie
+
+This repository implements a `custom` task for `image-to-image` for 🤗 Inference Endpoints to allow image up scaling by doubling image resolution. 
+The code for the customized pipeline is in the handler.py.
+
+To use deploy this model an Inference Endpoint you have to select `Custom` as task to use the `handler.py` file. 
+
+### expected Request payload
+
+Image to be labeled as binary. 
+
+#### CURL
+
+```
+curl URL \
+ -X POST \
+ --data-binary @car.png \
+ -H "Content-Type: image/png"
+```
+
+#### Python
+
+```python
+requests.post(ENDPOINT_URL, headers={"Content-Type": "image/png"}, data=open("car.png", 'rb').read()).json()
+```

handler.py

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+from image_gen_aux import UpscaleWithModel
+import numpy as np
+from PIL import Image
+from io import BytesIO
+import base64
+
+
+class EndpointHandler():
+ def __init__(self, path=""):
+ self.upscaler = UpscaleWithModel.from_pretrained("OzzyGT/DAT_X2").to("cuda")
+
+ def __call__(self, data):
+ img = data.pop("inputs", data)
+ output = self.upscaler(img, tiling=True, tile_width=768, tile_height=768, overlap=8)
+
+ buffered = BytesIO()
+ output.save(buffered, format="JPEG")
+ img_str = base64.b64encode(buffered.getvalue())
+
+ return img_str.decode()

requirements.txt

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+git+https://github.com/asomoza/image_gen_aux.git