rgres commited on
Commit
1273ba8
1 Parent(s): 68da561

Upload folder using huggingface_hub

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Files changed (5) hide show
  1. README.md +50 -0
  2. handdrawn.png +0 -0
  3. handler.py +1 -1
  4. output.png +0 -0
  5. test_endpoint.py +44 -0
README.md CHANGED
@@ -8,3 +8,53 @@ inference: true
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  ---
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  # 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)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  # 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)
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+
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+ You can call the inference endpoint like this :
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+
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+ ```
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+ import base64
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+ import requests
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+
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+ API_URL = "https://zqz606ggn85ysase.us-east-1.aws.endpoints.huggingface.cloud"
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+
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+ def encode_image(image_path):
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+ with open(image_path, "rb") as i:
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+ b64 = base64.b64encode(i.read())
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+ return b64.decode("utf-8")
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+
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+ prompt = "aerial view of jardin princier, Toulouse. Flowers, flowers, garden"
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+ image = encode_image("handdrawn.png")
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+
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+
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+ headers = {
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+ "Accept": "image/png",
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+ "Content-Type": "application/json"
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+ }
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+
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+ # test the handler
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+ def query(payload):
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+ response = requests.post(API_URL, headers=headers, json=payload)
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+ return response.content
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+
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+ payload = {
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+ "inputs": prompt,
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+ "prompt": prompt,
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+ "image": image,
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+ "steps": 20,
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+ "seed": 999
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+ }
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+
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+ import json
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+ with open('payload.json', 'w') as f:
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+ json.dump(payload, f)
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+
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+ image_bytes = query(payload)
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+
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+ # You can access the image with PIL.Image for example
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+ import io
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+ from PIL import Image
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+ image = Image.open(io.BytesIO(image_bytes))
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+
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+ image.save("output.png")
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+
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+ ```
handdrawn.png ADDED
handler.py CHANGED
@@ -40,7 +40,7 @@ class EndpointHandler():
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  # decode image
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  image = self.decode_base64_image(image)
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- self.generator = torch.Generator(device="cpu").manual_seed(3)
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  # run inference pipeline
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  image_out = self.pipe(
 
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  # decode image
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  image = self.decode_base64_image(image)
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+ self.generator = torch.Generator(device="cpu").manual_seed(seed)
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  # run inference pipeline
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  image_out = self.pipe(
output.png ADDED
test_endpoint.py ADDED
@@ -0,0 +1,44 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ import base64
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+ import requests
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+
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+ API_URL = "https://zqz606ggn85ysase.us-east-1.aws.endpoints.huggingface.cloud"
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+
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+ def encode_image(image_path):
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+ with open(image_path, "rb") as i:
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+ b64 = base64.b64encode(i.read())
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+ return b64.decode("utf-8")
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+
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+ prompt = "aerial view of jardin princier, Toulouse. Flowers, flowers, garden"
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+ image = encode_image("handdrawn.png")
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+
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+
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+ headers = {
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+ "Accept": "image/png",
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+ "Content-Type": "application/json"
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+ }
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+
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+ # test the handler
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+ def query(payload):
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+ response = requests.post(API_URL, headers=headers, json=payload)
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+ return response.content
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+
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+ payload = {
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+ "inputs": prompt,
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+ "prompt": prompt,
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+ "image": image,
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+ "steps": 20,
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+ "seed": 999
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+ }
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+
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+ import json
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+ with open('payload.json', 'w') as f:
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+ json.dump(payload, f)
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+
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+ image_bytes = query(payload)
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+
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+ # You can access the image with PIL.Image for example
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+ import io
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+ from PIL import Image
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+ image = Image.open(io.BytesIO(image_bytes))
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+
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+ image.save("output.png")