nodes/Icyman86-ComfyUI_AnimeCharacterSelect
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Icyman86
2025-06-29 13:32:41 +02:00
parent f081f9bcd2
commit 9adbc32d47
2 changed files with 29 additions and 102 deletions
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__init__ - Copy.py
-76
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@@ -1,76 +0,0 @@
import os
import json
import base64
class MinimalCharacterActionPrompt:
"""Minimal node: select character + action, output prompt + preview image"""
# Verzamel alle JSON-bestanden (output_1.json t/m output_11.json)
CHARACTER_JSON_FILES = [
os.path.join(os.path.dirname(__file__), f"output_{i}.json") for i in range(1, 12)
]
ACTION_JSON = os.path.join(os.path.dirname(__file__), "action.json")
# Initialiseer class variables
char_data = []
action_data = {}
CHARACTERS = []
ACTIONS = []
# Laad JSON-data bij class loading
try:
for path in CHARACTER_JSON_FILES:
with open(path, "r", encoding="utf-8") as f:
char_data.extend(json.load(f))
with open(ACTION_JSON, "r", encoding="utf-8") as f:
action_data = json.load(f)
CHARACTERS = [list(entry.keys())[0] for entry in char_data]
ACTIONS = list(action_data.keys())
except Exception as e:
print("❌ JSON load error:", e)
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"character": (cls.CHARACTERS,),
"action": (cls.ACTIONS,),
"extra_prompt": ("STRING", {"multiline": True, "default": ""}),
"clip": ("CLIP",), # Alleen voor compatibiliteit
}
}
RETURN_TYPES = ("STRING", "STRING",)
RETURN_NAMES = ("prompt", "preview_image_base64",)
FUNCTION = "build_prompt"
CATEGORY = "Prompting/Anime Character"
def build_prompt(self, character, action, extra_prompt, clip):
char_prompt = ""
preview_image = ""
action_prompt = self.action_data.get(action, "")
for entry in self.char_data:
if character in entry:
char_prompt = entry[character]
if "preview" in entry:
img_path = os.path.join(os.path.dirname(__file__), entry["preview"])
if os.path.isfile(img_path):
try:
with open(img_path, "rb") as img:
preview_image = base64.b64encode(img.read()).decode("utf-8")
except Exception as e:
print(f"❌ Fout bij laden preview afbeelding voor {character}:", e)
break
final_prompt = ", ".join(filter(None, [char_prompt, action_prompt, extra_prompt]))
return (final_prompt, preview_image)
NODE_CLASS_MAPPINGS = {
"MinimalCharacterActionPrompt": MinimalCharacterActionPrompt
}
NODE_DISPLAY_NAME_MAPPINGS = {
"MinimalCharacterActionPrompt": "Character + Action Prompt (WIP)"
}
__init__.py
+29 -26
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@@ -1,9 +1,8 @@
import os
import json
import base64
import numpy as np
import torch
import cv2
from PIL import Image
from io import BytesIO
class EnhancedCharacterPromptNode:
"""ComfyUI node: kies character + action, toon preview image, output prompt + conditioning"""
@@ -50,23 +49,34 @@ class EnhancedCharacterPromptNode:
}
RETURN_TYPES = ("STRING", "IMAGE", "CONDITIONING")
RETURN_NAMES = ("prompt", "preview_image", "conditioning")
RETURN_NAMES = ("prompt", "preview_image", "CONDITIONING")
FUNCTION = "build_prompt"
CATEGORY = "Prompting/Anime Character"
def build_prompt(self, character, action, extra_prompt, clip):
char_prompt = ""
action_prompt = self.action_data.get(action, "")
image_tensor = None
preview_image = None
for entry in self.char_data:
if isinstance(entry, dict) and character in entry:
char_prompt = entry[character]
preview_data = entry.get("preview", "")
if preview_data.startswith("data:image"):
value = entry[character]
# Data in the json files can either be of the form
# {"character name": "prompt", "preview": "data:image..."}
# or {"prompt": "data:image..."}. Detect which one we have
# by checking the value of the selected key.
if isinstance(value, str) and value.startswith("data:image"):
char_prompt = character
preview_data = value
else:
char_prompt = value
preview_data = entry.get("preview", "")
if isinstance(preview_data, str) and preview_data.startswith("data:image"):
try:
base64_data = preview_data.split("base64,", 1)[1]
image_tensor = self.decode_base64_to_tensor(base64_data)
preview_image = self.decode_base64_to_image(base64_data)
except Exception as e:
print(f"⚠️ Base64 decode failed for {character}: {e}")
break
@@ -75,25 +85,18 @@ class EnhancedCharacterPromptNode:
# Conditionering via CLIP als beschikbaar
conditioning = clip.encode(final_prompt) if clip else None
if conditioning is not None and not isinstance(conditioning, dict):
conditioning = {"conditioning": conditioning}
return (final_prompt, image_tensor, conditioning)
return (final_prompt, preview_image, conditioning)
def decode_base64_to_tensor(self, base64_str):
nparr = np.frombuffer(base64.b64decode(base64_str), np.uint8)
img = cv2.imdecode(nparr, cv2.IMREAD_UNCHANGED)
if img is None:
raise ValueError("Failed to decode base64 image")
if img.shape[2] == 4:
alpha = img[:, :, 3]
img = cv2.cvtColor(img, cv2.COLOR_BGRA2RGB)
mask = torch.from_numpy((alpha / 255.0).astype(np.float32)).unsqueeze(0)
else:
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
mask = torch.ones((1, img.shape[0], img.shape[1]), dtype=torch.float32)
img = img.astype(np.float32) / 255.0
img_tensor = torch.from_numpy(img).permute(2, 0, 1).unsqueeze(0) # [1, 3, H, W]
return img_tensor
def decode_base64_to_image(self, base64_str):
data = base64.b64decode(base64_str)
try:
img = Image.open(BytesIO(data)).convert("RGB")
except Exception as e:
raise ValueError("Failed to decode base64 image") from e
return img
NODE_CLASS_MAPPINGS = {