Add files via upload

__init__.py

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+from .topaz_upscaler import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']

topaz_upscaler.py

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+import os
+import time
+import requests
+import tempfile
+import numpy as np
+from PIL import Image
+import io
+
+BASE_URL = "https://api.topazlabs.com/image/v1"
+
+class TopazUpscaler:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "api_key": ("STRING", {
+                    "multiline": False,
+                    "default": "",
+                    "placeholder": "Enter your Topaz API key"
+                }),
+                "output_width": ("INT", {
+                    "default": 3840,
+                    "min": 512,
+                    "max": 16000,
+                    "step": 64
+                }),
+                "crop_to_fill": ("BOOLEAN", {"default": False}),
+            },
+            "optional": {}
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("upscaled_image",)
+    FUNCTION = "upscale"
+    CATEGORY = "image/upscaling"
+
+    def _submit_job(self, image_path, api_key, output_width, crop_to_fill):
+        headers = {
+            "X-API-Key": api_key,
+            "accept": "application/json"
+        }
+        with open(image_path, 'rb') as f:
+            files = {'image': f}
+            data = {
+                "output_width": output_width,
+                "crop_to_fill": str(crop_to_fill).lower(),
+                "output_format": "jpeg"
+            }
+            response = requests.post(f"{BASE_URL}/enhanceAsync", headers=headers, files=files, data=data)
+        response.raise_for_status()
+        return response.json()["task_id"]
+
+    def _wait_for_completion(self, task_id, api_key, timeout=300):
+        headers = {"X-API-Key": api_key}
+        start_time = time.time()
+        while time.time() - start_time < timeout:
+            resp = requests.get(f"{BASE_URL}/getStatus?task_id={task_id}", headers=headers)
+            resp.raise_for_status()
+            status = resp.json()
+            if status["status"] == "completed":
+                return True
+            elif status["status"] == "failed":
+                raise Exception(f"Topaz task {task_id} failed: {status.get('error', 'Unknown error')}")
+            time.sleep(5)
+        raise TimeoutError(f"Topaz task {task_id} timed out after {timeout} seconds.")
+
+    def _download_result(self, task_id, api_key):
+        headers = {
+            "X-API-Key": api_key,
+            "accept": "image/jpeg"
+        }
+        resp = requests.get(f"{BASE_URL}/getDownloadOutput?task_id={task_id}", headers=headers)
+        resp.raise_for_status()
+        return resp.content
+
+    def upscale(self, image, api_key, output_width, crop_to_fill):
+        if not api_key or api_key.strip() == "":
+            raise ValueError("Topaz API key is required. Please enter it in the node.")
+
+        # Convert ComfyUI image tensor [B,H,W,C] float32 [0,1] -> PIL Image
+        img_np = (image[0].cpu().numpy() * 255).astype(np.uint8)
+        pil_image = Image.fromarray(img_np)
+
+        # Save to temp file
+        with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as tmp_input:
+            pil_image.save(tmp_input.name, format="JPEG", quality=95)
+            input_path = tmp_input.name
+
+        try:
+            print(f"[Topaz Upscaler] Submitting job with width={output_width}, crop={crop_to_fill}")
+            task_id = self._submit_job(input_path, api_key, output_width, crop_to_fill)
+            print(f"[Topaz Upscaler] Task ID: {task_id}")
+
+            print(f"[Topaz Upscaler] Waiting for completion...")
+            self._wait_for_completion(task_id, api_key)
+
+            print(f"[Topaz Upscaler] Downloading result...")
+            result_bytes = self._download_result(task_id, api_key)
+
+            # Load result
+            result_pil = Image.open(io.BytesIO(result_bytes))
+            result_np = np.array(result_pil).astype(np.float32) / 255.0
+            result_tensor = result_np[np.newaxis, ...]  # Add batch dim
+
+            return (result_tensor,)
+
+        finally:
+            # Clean up input temp file
+            if os.path.exists(input_path):
+                os.unlink(input_path)
+
+
+# Node registration
+NODE_CLASS_MAPPINGS = {
+    "TopazUpscaler": TopazUpscaler
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "TopazUpscaler": "Topaz Upscaler"
+}
+
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']