update

__init__.py

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+from .swapper import NODE_CLASS_MAPPINGS
+
+__all__ = ['NODE_CLASS_MAPPINGS']

swapper.py

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+import numpy as np
+from .utils import faceswapper
+
+class tagger_node:
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("Output",)
+    FUNCTION = "swap_face"
+    CATEGORY = "5x00/"
+
+    @classmethod
+    def IS_CHANGED(cls, **kwargs):
+        return float("NaN")
+    
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "Image" : ("IMAGE", {}), 
+                "Face" : ("IMAGE", {}),
+                "API_Key" : ("STRING", {}),
+            },
+        }
+
+    def swap_face(Image, Face, API_Key):
+        swapped_image = faceswapper(Image, Face, API_Key)
+        return (swapped_image,)
+    
+NODE_CLASS_MAPPINGS = {
+    "Face Swapper" : tagger_node,
+}

utils.py

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+import PIL
+import numpy as np
+import base64
+import requests
+import json
+import PIL
+from io import BytesIO
+import torch
+import time
+
+def tensor_to_image(tensor):
+    tensor = tensor*255
+    tensor = np.array(tensor, dtype=np.uint8)
+    if np.ndim(tensor)>3:
+        assert tensor.shape[0] == 1
+        tensor = tensor[0]
+    return PIL.Image.fromarray(tensor)
+
+def fetch_image(url, payload, headers, timeout=3, interval=0.5):
+    start_time = time.time()
+    
+    while time.time() - start_time < timeout:
+        # Make the GET request
+        response = requests.request("GET", url, headers=headers, json=payload)
+        
+        if response.code == 200:
+            data = response.json()
+            
+            # Check if the response has the expected data (e.g., status is not "pending")
+            if data.get("data", {}).get("status") != "pending":
+                return data  # Response is ready
+        
+        # Wait before the next poll
+        time.sleep(interval)
+    
+    # If the timeout is reached, return None or perform another action
+    return None
+
+def image_to_bs64(Image: torch.Tensor) -> str:
+    # Convert and resize image
+    pil_image = tensor_to_image(Image)
+    max_dimension = 512
+    pil_image.thumbnail((max_dimension, max_dimension))
+    buffer = BytesIO()
+    pil_image.save(buffer, format="JPEG")
+    buffer.seek(0) 
+    base64_image = base64.b64encode(buffer.getvalue()).decode("utf-8")
+    buffer.close()
+    return base64_image
+
+def url_to_tensor(image_url: str) -> tuple[torch.Tensor]:
+    # Fetch the image data from the URL
+    response = requests.get(image_url)
+    response.raise_for_status()  # Raise an error if the request fails
+    image_data = response.content
+
+    # Open the image with PIL and handle EXIF orientation
+    image = PIL.Image.open(BytesIO(image_data))
+    image = PIL.ImageOps.exif_transpose(image)
+
+    # Convert to RGBA for potential alpha channel handling
+    image = image.convert("RGBA")
+    image_np = np.array(image).astype(np.float32) / 255.0  # Normalize
+
+    # Split the image into RGB and Alpha channels
+    rgb_np, alpha_np = image_np[..., :3], image_np[..., 3]
+
+    # Convert RGB to PyTorch tensor and ensure it's in the [N, H, W, C] format
+    tensor_image = torch.from_numpy(rgb_np).unsqueeze(0)  # Adds N dimension
+
+    return tensor_image
+
+def faceswapper(Image, Face, API_Key):
+
+    url = "https://api.piapi.ai/api/v1/task"
+
+    payload = json.dumps({
+    "model": "Qubico/image-toolkit",
+    "task_type": "face-swap",
+    "input": {
+        "target_image": image_to_bs64(Image),
+        "swap_image": image_to_bs64(Face)
+    }
+    })
+    headers = {
+    'x-api-key': API_Key,
+    'Content-Type': 'application/json'
+    }
+
+    response = requests.request("POST", url, headers=headers, data=payload)
+    response_data = response.json()
+    task_url = url + f'/{response_data.get("data", {}).get("task_id", None)}'
+
+    img_response = requests.request("GET", task_url, headers=headers, data='')
+    out_url = img_response.get("data", {}).get("image_url", None)
+    img_out = url_to_tensor(out_url)
+    
+    return img_out