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README.md

@@ -8,3 +8,53 @@ inference: true
 ---
 
 # Inference Endpoint for [Seg2Sat](https://huggingface.co/rgres/Seg2Sat-sd-controlnet) using [runwayml/stable-diffusion-v1-5](https://huggingface.co/stabilityai/stable-diffusion-2-1-base)
+
+You can call the inference endpoint like this :
+
+```
+import base64
+import requests
+
+API_URL = "https://zqz606ggn85ysase.us-east-1.aws.endpoints.huggingface.cloud"
+
+def encode_image(image_path):
+  with open(image_path, "rb") as i:
+    b64 = base64.b64encode(i.read())
+  return b64.decode("utf-8")
+
+prompt = "aerial view of jardin princier, Toulouse. Flowers, flowers, garden"
+image = encode_image("handdrawn.png")
+
+
+headers = {
+	"Accept": "image/png",
+	"Content-Type": "application/json"
+}
+
+# test the handler
+def query(payload):
+	response = requests.post(API_URL, headers=headers, json=payload)
+	return response.content
+
+payload = {
+  "inputs": prompt,
+  "prompt": prompt,
+  "image": image,
+  "steps": 20,
+  "seed": 999
+}
+
+import json
+with open('payload.json', 'w') as f:
+    json.dump(payload, f)
+
+image_bytes = query(payload)
+
+# You can access the image with PIL.Image for example
+import io
+from PIL import Image
+image = Image.open(io.BytesIO(image_bytes))
+
+image.save("output.png")
+
+```

handler.py

@@ -40,7 +40,7 @@ class EndpointHandler():
         # decode image
         image = self.decode_base64_image(image)
 
-        self.generator = torch.Generator(device="cpu").manual_seed(3)
+        self.generator = torch.Generator(device="cpu").manual_seed(seed)
         
         # run inference pipeline
         image_out = self.pipe(

test_endpoint.py

@@ -0,0 +1,44 @@
+import base64
+import requests
+
+API_URL = "https://zqz606ggn85ysase.us-east-1.aws.endpoints.huggingface.cloud"
+
+def encode_image(image_path):
+  with open(image_path, "rb") as i:
+    b64 = base64.b64encode(i.read())
+  return b64.decode("utf-8")
+
+prompt = "aerial view of jardin princier, Toulouse. Flowers, flowers, garden"
+image = encode_image("handdrawn.png")
+
+
+headers = {
+	"Accept": "image/png",
+	"Content-Type": "application/json"
+}
+
+# test the handler
+def query(payload):
+	response = requests.post(API_URL, headers=headers, json=payload)
+	return response.content
+
+payload = {
+  "inputs": prompt,
+  "prompt": prompt,
+  "image": image,
+  "steps": 20,
+  "seed": 999
+}
+
+import json
+with open('payload.json', 'w') as f:
+    json.dump(payload, f)
+
+image_bytes = query(payload)
+
+# You can access the image with PIL.Image for example
+import io
+from PIL import Image
+image = Image.open(io.BytesIO(image_bytes))
+
+image.save("output.png")