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# By WASasquatch (Discord: WAS#0263)
import torch, os, json, random, hashlib
from urllib.request import urlopen
import json
class WAS_NSP_CLIPTextEncoder:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"noodle_key": ("STRING", {"default": '__', "multiline": False}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"text": ("STRING", {"multiline": True}),
"clip": ("CLIP",),
}
}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "nsp_encode"
CATEGORY = "conditioning"
def nsp_encode(self, clip, text, noodle_key = '__', seed = 0):
# Fetch the NSP Pantry
local_pantry = 'ComfyUI/custom_nodes/nsp_pantry.json'
if not os.path.exists(local_pantry):
response = urlopen('https://raw.githubusercontent.com/WASasquatch/noodle-soup-prompts/main/nsp_pantry.json')
tmp_pantry = json.loads(response.read())
# Dump JSON locally
pantry_serialized = json.dumps(tmp_pantry, indent=4)
with open(local_pantry, "w") as f:
f.write(pantry_serialized)
del response, tmp_pantry
# Load local pantry
with open(local_pantry, 'r') as f:
nspterminology = json.load(f)
if seed > 0 or seed < 1:
random.seed(seed)
# Parse Text
new_text = text
for term in nspterminology:
# Target Noodle
tkey = f'{noodle_key}{term}{noodle_key}'
# How many occurances?
tcount = new_text.count(tkey)
# Apply random results for each noodle counted
for _ in range(tcount):
new_text = new_text.replace(tkey, random.choice(nspterminology[term]), 1)
seed = seed+1
random.seed(seed)
print('Parsed Prompt:', new_text)
return ([[clip.encode(new_text), {}]], )
NODE_CLASS_MAPPINGS = {
"CLIPTextEncode (NSP)": WAS_NSP_CLIPTextEncoder
}
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