Upload 11 files (#1)

- Upload 11 files (6402e73fcb3b72796de40f3d5936967fe92b5b69)
- Update requirements.txt (716062eb782ef9a0f1299544cde847f84f86fe51)

README.md

@@ -35,7 +35,6 @@ There is also a [notebook](https://huggingface.co/philschmid/ControlNet-endpoint
     "negative_prompt": "low res, bad anatomy, worst quality, low quality",
     "controlnet_type": "depth",
     "image" : "iVBORw0KGgoAAAANSUhEUgAAAgAAAAIACAIAAAB7GkOtAAAABGdBTUEAALGPC",
-    "mask_image": "iVBORw0KGgoAAAANSUhEUgAAAgAAAAIACAIAAAB7GkOtAAAABGdBTUEAALGPC",
 }
 ```
 
@@ -61,8 +60,8 @@ import base64
 from PIL import Image
 from io import BytesIO
 
-ENDPOINT_URL = ""
-HF_TOKEN = ""
+ENDPOINT_URL = "" # your endpoint url
+HF_TOKEN = "" # your huggingface token `hf_xxx`
 
 # helper image utils
 def encode_image(image_path):
@@ -73,10 +72,9 @@ def encode_image(image_path):
 
 def predict(prompt, image, negative_prompt=None, controlnet_type = "normal"):
     image = encode_image(image)
-    mask_image = encode_image(mask_image)
 
     # prepare sample payload
-    request = {"prompt": prompt, "image": image, "negative_prompt": negative_prompt, "controlnet_type": controlnet_type}
+    request = {"inputs": prompt, "image": image, "negative_prompt": negative_prompt, "controlnet_type": controlnet_type}
     # headers
     headers = {
         "Authorization": f"Bearer {HF_TOKEN}",
@@ -84,17 +82,24 @@ def predict(prompt, image, negative_prompt=None, controlnet_type = "normal"):
         "Accept": "image/png" # important to get an image back
     }
 
-    response = r.post(ENDPOINT_URL, headers=headers, json=payload)
+    response = r.post(ENDPOINT_URL, headers=headers, json=request)
+    if response.status_code != 200:
+        print(response.text)
+        raise Exception("Prediction failed")
     img = Image.open(BytesIO(response.content))
     return img
 
+
 prediction = predict(
-  prompt = "best quality, extremely detailed",
-  negative_prompt ="lowres, bad anatomy, worst quality, low quality",
-  controlnet_type = "depth",
-  image = encode_image("input_image_vermeer.png")
+  prompt = "cloudy sky background lush landscape house and green trees, RAW photo (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3",
+  negative_prompt ="lowres, bad anatomy, worst quality, low quality, city, traffic",
+  controlnet_type = "hed",
+  image = "huggingface.png"
 )
 
+prediction.save("result.png")
+```
+
 ```
 expected output
 

requirements.txt

@@ -1,6 +1,5 @@
 
 git+https://github.com/huggingface/diffusers.git
 safetensors 
-#xformers 
 opencv-python
 controlnet_hinter==0.0.5