EnragedAntelope-ComfyUI_EasyRegion/MultiAreaConditioning/MultiAreaConditioning.py

# Made by Davemane42

import torch
from nodes import MAX_RESOLUTION

class MultiAreaConditioning:
    def __init__(self) -> None:
        pass

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "conditioning0": ("CONDITIONING", ),
                "conditioning1": ("CONDITIONING", )
            },
            "hidden": {"extra_pnginfo": "EXTRA_PNGINFO", "unique_id": "UNIQUE_ID"},
        }
    


    RETURN_TYPES = ("CONDITIONING", "INT", "INT")
    RETURN_NAMES = (None, "resolutionX", "resolutionY")
    FUNCTION = "doStuff"
    CATEGORY = "Davemane42"

    def doStuff(self, extra_pnginfo, unique_id, **kwargs):

        c = []
        values = []
        resolutionX = 512
        resolutionY = 512

        for node in extra_pnginfo["workflow"]["nodes"]:
            if node["id"] == int(unique_id):
                values = node["properties"]["values"]
                resolutionX = node["properties"]["width"]
                resolutionY = node["properties"]["height"]
                break
        k = 0
        for arg in kwargs:
            if k > len(values): break;
            if not torch.is_tensor(kwargs[arg][0][0]): continue;
            
            x, y = values[k][0], values[k][1]
            w, h = values[k][2], values[k][3]

            # If fullscreen
            if (x == 0 and y == 0 and w == resolutionX and h == resolutionY):
                for t in kwargs[arg]:
                    c.append(t)
                k += 1
                continue
            
            if x+w > resolutionX:
                w = max(0, resolutionX-x)
            
            if y+h > resolutionY:
                h = max(0, resolutionY-y)

            if w == 0 or h == 0: continue;

            for t in kwargs[arg]:
                n = [t[0], t[1].copy()]
                n[1]['area'] = (h // 8, w // 8, y // 8, x // 8)
                n[1]['strength'] = 1.0
                n[1]['min_sigma'] = 0.0
                n[1]['max_sigma'] = 99.0
                
                c.append(n)
            
            k += 1
            
        
        return (c, resolutionX, resolutionY)

class ConditioningUpscale():
    def __init__(self) -> None:
        pass

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "conditioning": ("CONDITIONING", ),
                "scalar": ("INT", {"default": 2, "min": 1, "max": 100, "step": 0.5}),
            },
        }
    
    RETURN_TYPES = ("CONDITIONING",)
    CATEGORY = "Davemane42"

    FUNCTION = 'upscale'

    def upscale(self, conditioning, scalar):
        c = []
        for t in conditioning:

            n = [t[0], t[1].copy()]
            if 'area' in n[1]:
                
                n[1]['area'] = tuple(map(lambda x: ((x*scalar + 7) >> 3) << 3, n[1]['area']))

            c.append(n)

        return (c, )
    
class ConditioningStretch():
    def __init__(self) -> None:
        pass

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "conditioning": ("CONDITIONING", ),
                "resolutionX": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
                "resolutionY": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
                "newWidth": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
                "newHeight": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
                #"scalar": ("INT", {"default": 2, "min": 1, "max": 100, "step": 0.5}),
            },
        }
    
    RETURN_TYPES = ("CONDITIONING",)
    CATEGORY = "Davemane42"

    FUNCTION = 'upscale'

    def upscale(self, conditioning, resolutionX, resolutionY, newWidth, newHeight, scalar=1):
        c = []
        for t in conditioning:

            n = [t[0], t[1].copy()]
            if 'area' in n[1]:

                newWidth *= scalar
                newHeight *= scalar
                
                #n[1]['area'] = tuple(map(lambda x: ((x*scalar + 32) >> 6) << 6, n[1]['area']))
                x = ((n[1]['area'][3]*8)*newWidth/resolutionX) // 8
                y = ((n[1]['area'][2]*8)*newHeight/resolutionY) // 8
                w = ((n[1]['area'][1]*8)*newWidth/resolutionX) // 8
                h = ((n[1]['area'][0]*8)*newHeight/resolutionY) // 8

                n[1]['area'] = tuple(map(lambda x: (((int(x) + 7) >> 3) << 3), [h, w, y, x]))

            c.append(n)

        return (c, )

class ConditioningDebug():
    def __init__(self) -> None:
        pass

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "conditioning": ("CONDITIONING", ),
            }
        }
    
    RETURN_TYPES = ()
    FUNCTION = "debug"

    OUTPUT_NODE = True

    CATEGORY = "Davemane42"

    def debug(self, conditioning):
        print("\nDebug")
        for i, t in enumerate(conditioning):
            print(f"{i}:")
            if "area" in t[1]:
                print(f"\tx{t[1]['area'][3]*8} y{t[1]['area'][2]*8} \n\tw{t[1]['area'][1]*8} h{t[1]['area'][0]*8} \n\tstrength: {t[1]['strength']}")
            else:
                print(f"\tFullscreen")

        return (None, )
            
NODE_CLASS_MAPPINGS = {
    "MultiAreaConditioning": MultiAreaConditioning,
    "ConditioningUpscale": ConditioningUpscale,
    "ConditioningStretch": ConditioningStretch,
    "ConditioningDebug": ConditioningDebug,
}