cedarconnor-upsampler/nodes.py

import os
import requests
import time
import tempfile
from typing import Tuple, Optional, Dict, Any
from urllib.parse import urlparse
from PIL import Image
import torch
import numpy as np

# Try to import ComfyUI modules with error handling
try:
    import folder_paths
    from comfy.utils import ProgressBar
    COMFYUI_AVAILABLE = True
except ImportError as e:
    print(f"ComfyUI imports failed: {e}")
    COMFYUI_AVAILABLE = False
    
    # Create dummy ProgressBar for testing
    class ProgressBar:
        def __init__(self, total):
            self.total = total
        def update(self, value):
            pass

# Simple test - just basic ImgBB functionality
class UpsamplerSmartUpscale:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "image": ("IMAGE",),
                "api_key": ("STRING", {"multiline": False}),
                "input_image_type": (["realism", "anime", "universal"],),
                "upscale_factor": ("FLOAT", {"default": 2.0, "min": 1.0, "max": 4.0, "step": 0.1}),
                "global_creativity": ("INT", {"default": 5, "min": 0, "max": 10}),
                "detail": ("INT", {"default": 5, "min": 0, "max": 10}),
            },
            "optional": {
                "imgbb_api_key": ("STRING", {"multiline": False, "default": ""}),
                "description": ("STRING", {"multiline": True, "default": ""}),
                "should_enhance_faces": ("BOOLEAN", {"default": False}),
                "should_preserve_blur": ("BOOLEAN", {"default": False}),
            }
        }

    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("upscaled_image",)
    FUNCTION = "upscale"
    CATEGORY = "Upsampler"

    def upscale(self, image: torch.Tensor, api_key: str, input_image_type: str, 
                upscale_factor: float, global_creativity: int, detail: int,
                imgbb_api_key: str = "", description: str = "", should_enhance_faces: bool = False, 
                should_preserve_blur: bool = False) -> Tuple[torch.Tensor]:
        
        print(f"🚀 [Upsampler] Starting minimal test...")
        
        # Just return the input image for now - this is a test
        return (image,)


class UpsamplerDynamicUpscale:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "image": ("IMAGE",),
                "api_key": ("STRING", {"multiline": False}),
                "input_image_type": (["realism", "anime", "universal"],),
                "upscale_factor": ("FLOAT", {"default": 2.0, "min": 1.0, "max": 4.0, "step": 0.1}),
                "global_creativity": ("INT", {"default": 5, "min": 0, "max": 10}),
                "resemblance": ("INT", {"default": 8, "min": 0, "max": 10}),
                "detail": ("INT", {"default": 5, "min": 0, "max": 10}),
            },
            "optional": {
                "imgbb_api_key": ("STRING", {"multiline": False, "default": ""}),
                "description": ("STRING", {"multiline": True, "default": ""}),
                "should_enhance_faces": ("BOOLEAN", {"default": False}),
                "should_preserve_hands": ("BOOLEAN", {"default": False}),
                "should_preserve_blur": ("BOOLEAN", {"default": False}),
            }
        }

    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("upscaled_image",)
    FUNCTION = "upscale"
    CATEGORY = "Upsampler"

    def upscale(self, image: torch.Tensor, api_key: str, input_image_type: str, 
                upscale_factor: float, global_creativity: int, resemblance: int, 
                detail: int, imgbb_api_key: str = "", description: str = "", should_enhance_faces: bool = False, 
                should_preserve_hands: bool = False, should_preserve_blur: bool = False) -> Tuple[torch.Tensor]:
        
        print(f"🚀 [Upsampler] Starting dynamic test...")
        return (image,)


class UpsamplerPreciseUpscale:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "image": ("IMAGE",),
                "api_key": ("STRING", {"multiline": False}),
                "input_image_type": (["realism", "anime", "universal"],),
                "upscale_factor": ("FLOAT", {"default": 4.0, "min": 1.0, "max": 16.0, "step": 0.1}),
            },
            "optional": {
                "imgbb_api_key": ("STRING", {"multiline": False, "default": ""}),
                "should_enhance_faces": ("BOOLEAN", {"default": False}),
                "should_preserve_blur": ("BOOLEAN", {"default": False}),
            }
        }

    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("upscaled_image",)
    FUNCTION = "upscale"
    CATEGORY = "Upsampler"

    def upscale(self, image: torch.Tensor, api_key: str, input_image_type: str, 
                upscale_factor: float, imgbb_api_key: str = "", should_enhance_faces: bool = False, 
                should_preserve_blur: bool = False) -> Tuple[torch.Tensor]:
        
        print(f"🚀 [Upsampler] Starting precise test...")
        return (image,)

print("✅ Minimal nodes loaded successfully")