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Replace ConditioningCombineWeighted with ConditioningAddWeighted and add resizing to account for apppend based combine and multiple clip sizes

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This commit is contained in:
Bryan Lyon 2023-02-09 00:46:58 -08:00
parent 73ea86e927
commit 8cb96284c6
1 changed files with 13 additions and 5 deletions
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18
nodes.py
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@ -9,6 +9,7 @@ import copy
from PIL import Image from PIL import Image
from PIL.PngImagePlugin import PngInfo from PIL.PngImagePlugin import PngInfo
import numpy as np import numpy as np
import torch
sys.path.append(os.path.join(sys.path[0], "comfy")) sys.path.append(os.path.join(sys.path[0], "comfy"))
@ -40,7 +41,7 @@ class CLIPTextEncode:
def encode(self, clip, text): def encode(self, clip, text):
return ([[clip.encode(text), {}]], ) return ([[clip.encode(text), {}]], )
class ConditioningCombineWeighted: class ConditioningAddWeighted:
@classmethod @classmethod
def INPUT_TYPES(s): def INPUT_TYPES(s):
return {"required": {"conditioning_1": ("CONDITIONING", ), "conditioning_2": ("CONDITIONING", ), return {"required": {"conditioning_1": ("CONDITIONING", ), "conditioning_2": ("CONDITIONING", ),
@ -48,13 +49,20 @@ class ConditioningCombineWeighted:
"conditioning_2_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.1}) "conditioning_2_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.1})
}} }}
RETURN_TYPES = ("CONDITIONING",) RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "combineWeighted" FUNCTION = "addWeighted"
CATEGORY = "conditioning" CATEGORY = "conditioning"
def combineWeighted(self, conditioning_1, conditioning_2, conditioning_1_strength, conditioning_2_strength): def addWeighted(self, conditioning_1, conditioning_2, conditioning_1_strength, conditioning_2_strength):
conditioning_1_tensor = conditioning_1[0][0]
conditioning_2_tensor = conditioning_2[0][0]
output = conditioning_1 output = conditioning_1
output[0][0] = (conditioning_1[0][0] * conditioning_1_strength + conditioning_2[0][0] * conditioning_2_strength)/(conditioning_1_strength + conditioning_2_strength) if conditioning_1_tensor.shape[1] > conditioning_2_tensor.shape[1]:
conditioning_2_tensor = torch.cat((conditioning_2_tensor, torch.zeros((1,conditioning_1_tensor.shape[1] - conditioning_2_tensor.shape[1],768))), dim=1)
elif conditioning_2_tensor.shape[1] > conditioning_1_tensor.shape[1]:
conditioning_1_tensor = torch.cat((conditioning_1_tensor, torch.zeros((1,conditioning_2_tensor.shape[1] - conditioning_1_tensor.shape[1],768))), dim=1)
output[0][0] = (conditioning_1_tensor * conditioning_1_strength + conditioning_2_tensor * conditioning_2_strength)/(conditioning_1_strength + conditioning_2_strength)
return (output, ) return (output, )
class ConditioningCombine: class ConditioningCombine:
@ -578,7 +586,7 @@ NODE_CLASS_MAPPINGS = {
"LoadImage": LoadImage, "LoadImage": LoadImage,
"ImageScale": ImageScale, "ImageScale": ImageScale,
"ConditioningCombine": ConditioningCombine, "ConditioningCombine": ConditioningCombine,
"ConditioningCombineWeighted": ConditioningCombineWeighted, "ConditioningAddWeighted": ConditioningAddWeighted,
"ConditioningSetArea": ConditioningSetArea, "ConditioningSetArea": ConditioningSetArea,
"KSamplerAdvanced": KSamplerAdvanced, "KSamplerAdvanced": KSamplerAdvanced,
"LatentComposite": LatentComposite, "LatentComposite": LatentComposite,