Upload wildcard_3.py

wildcard_3.py

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+import random
+
+class Wildcard_3:
+    """
+    ComfyUI custom node that generates two deterministic wildcard-style prompts
+    (female and male variation) from a given seed.
+
+    Each prompt roughly follows:
+
+        1girl, straight-on, woman,
+        rich woman, sadistic woman,
+        fair skincolor, blue eyes, sharp eyes, looking at viewer eyes,
+        silver hair, very long hair, wavy hair,
+        red blouse, linen blouse, checkered blouse,
+        brown skirt, knee-length skirt, pleated skirt,
+        black pants, denim pants, baggy pants,
+        grey boots, ankle-high boots, lace-up boots,
+        black tiara headwear.
+
+    Male variant mirrors it with male age / gender words:
+
+        1boy, straight-on, man,
+        rich man, sadistic man,
+        ...
+
+    The same seed always gives the same pair of prompts.
+    """
+
+    CATEGORY = "prompt/wildcard"
+    FUNCTION = "generate"
+    RETURN_TYPES = ("STRING", "STRING")
+    RETURN_NAMES = ("female_prompt", "male_prompt")
+    OUTPUT_NODE = False
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                # Concept seed. Same seed => same pair of prompts.
+                "seed": ("INT", {
+                    "default": 0,
+                    "min": 0,
+                    "max": 2**31 - 1,
+                    "step": 1,
+                }),
+            },
+        }
+
+    # ---- Helper methods -------------------------------------------------
+
+    @staticmethod
+    def _rng(seed: int) -> random.Random:
+        """Deterministic RNG helper."""
+        return random.Random(int(seed))
+
+    @staticmethod
+    def _choose(rng: random.Random, items):
+        return items[rng.randrange(len(items))]
+
+    @classmethod
+    def _build_common_concept(cls, rng: random.Random):
+        """
+        Build the gender-neutral part of the character concept:
+        camera POV, age stage, skin, eyes, hair, clothes, etc.
+        """
+        # --- Camera POV (Category 2) --------------------------------------
+        # LEFTSIDE / RIGHTSIDE stay in ALL CAPS
+        pov_options = [
+            # each entry is a list of 1–2 tokens
+            ["straight-on"],
+            ["straight-on", "frontview"],
+            ["diagonal LEFTSIDE", "three-quarter view LEFTSIDE"],
+            ["sideview LEFTSIDE", "from LEFTSIDE side"],
+            ["from front POV", "frontview POV"],
+            ["sideview RIGHTSIDE", "from RIGHTSIDE side"],
+            ["diagonal RIGHTSIDE", "three-quarter view RIGHTSIDE"],
+            ["slight high-angle", "from above POV"],
+            ["low-angle", "from below POV"],
+        ]
+        pov_tokens = cls._choose(rng, pov_options)
+
+        # --- Age stage / category 3 base ---------------------------------
+        stage = cls._choose(rng, ["teen", "adult", "elder"])
+
+        if stage == "teen":
+            cat3_female = "girl"
+            cat3_male = "boy"
+        elif stage == "adult":
+            cat3_female = "woman"
+            cat3_male = "man"
+        else:
+            cat3_female = "loli"
+            cat3_male = "shota"
+
+        # --- Social and inner personality (Category 4 style) -------------
+        # Two tokens, second word identical within each prompt:
+        # e.g. "rich woman, sadistic woman"
+        outer_traits = [
+            "rich",
+            "well-dressed",
+            "casual",
+            "elegant",
+            "gothic",
+            "punk",
+            "corporate",
+            "sporty",
+            "nerdy",
+            "streetwise",
+        ]
+        inner_traits = [
+            "sadistic",
+            "kind",
+            "cold",
+            "gentle",
+            "playful",
+            "serious",
+            "melancholic",
+            "anxious",
+            "confident",
+            "calm",
+        ]
+
+        outer_trait = cls._choose(rng, outer_traits)
+        # ensure inner != outer for a bit of contrast
+        possible_inner = [t for t in inner_traits if t != outer_trait]
+        inner_trait = cls._choose(rng, possible_inner)
+
+        # --- Skin colour (no "colored") ----------------------------------
+        skin_tones = [
+            "pale skincolor",
+            "fair skincolor",
+            "light tan skincolor",
+            "olive skincolor",
+            "golden brown skincolor",
+            "medium brown skincolor",
+            "dark brown skincolor",
+            "deep brown skincolor",
+        ]
+        skin_token = cls._choose(rng, skin_tones)
+
+        # --- Eyes (Category 5) -------------------------------------------
+        eye_colors = [
+            "blue eyes",
+            "green eyes",
+            "light brown eyes",
+            "dark brown eyes",
+            "hazel eyes",
+            "grey eyes",
+            "amber eyes",
+        ]
+        eye_styles = [
+            "sharp eyes",
+            "narrow eyes",
+            "round eyes",
+            "large eyes",
+            "tired eyes",
+            "bright eyes",
+            "soft eyes",
+        ]
+        eye_directions = [
+            "",
+        ]
+
+        eye_color = cls._choose(rng, eye_colors)
+        eye_style = cls._choose(rng, eye_styles)
+        eye_direction = cls._choose(rng, eye_directions)
+
+        # --- Hair (Category 6: color -> length -> style) -----------------
+        # Haircolor never uses "red hair"; we use "ginger hair" instead.
+        hair_colors = [
+            "black hair",
+            "dark brown hair",
+            "light brown hair",
+            "blonde hair",
+            "platinum blonde hair",
+            "ginger hair",
+            "white hair",
+            "silver hair",
+            "ash brown hair",
+        ]
+        hair_lengths = [
+            "short hair",
+            "medium length hair",
+            "shoulder length hair",
+            "long hair",
+            "very long hair",
+        ]
+        hair_styles = [
+            "straight hair",
+            "wavy hair",
+            "curly hair",
+            "messy hair",
+            "neatly combed hair",
+            "braided hair",
+            "ponytail hair",
+            "bun hair",
+        ]
+
+        hair_color = cls._choose(rng, hair_colors)
+        hair_length = cls._choose(rng, hair_lengths)
+        hair_style = cls._choose(rng, hair_styles)
+
+        # --- Top garment (Category 7) ------------------------------------
+        # color + material + adjective, but same garment word
+        top_items = [
+            "shirt",
+            "blouse",
+            "turtleneck",
+            "hoodie",
+            "sweater",
+            "jacket",
+            "coat",
+        ]
+        top_colors = [
+            "red",
+            "blue",
+            "green",
+            "yellow",
+            "black",
+            "white",
+            "grey",
+            "brown",
+            "beige",
+            "navy",
+            "burgundy",
+        ]
+        top_materials = [
+            "cotton",
+            "linen",
+            "silk",
+            "wool",
+            "denim",
+            "leather",
+            "polyester",
+        ]
+        top_styles = [
+            "plain",
+            "striped",
+            "checkered",
+            "printed",
+            "torn",
+            "fitted",
+            "oversized",
+        ]
+
+        top_item = cls._choose(rng, top_items)
+        top_color = cls._choose(rng, top_colors)
+        top_material = cls._choose(rng, top_materials)
+        top_style = cls._choose(rng, top_styles)
+
+        top_tokens = [
+            f"{top_color} {top_item}",
+            f"{top_material} {top_item}",
+            f"{top_style} {top_item}",
+        ]
+
+        # --- Skirt (Category 8) ------------------------------------------
+        skirt_colors = top_colors  # reuse palette
+        skirt_lengths = [
+            "short skirt",
+            "knee-length skirt",
+            "midi skirt",
+            "ankle-length skirt",
+        ]
+        skirt_styles = [
+            "pleated skirt",
+            "denim skirt",
+            "wool skirt",
+            "cotton skirt",
+            "pencil skirt",
+            "A-line skirt",
+        ]
+
+        skirt_color = cls._choose(rng, skirt_colors)
+        skirt_length = cls._choose(rng, skirt_lengths)
+        skirt_style = cls._choose(rng, skirt_styles)
+
+        skirt_tokens = [
+            f"{skirt_color} skirt",
+            skirt_length,
+            skirt_style,
+        ]
+
+        # --- Bottom garment (Category 9) ---------------------------------
+        # color + material + adjective, same garment word
+        bottom_items = [
+            "pants",
+            "jeans",
+            "trousers",
+            "shorts",
+            "leggings",
+            "sweatpants",
+        ]
+        bottom_colors = top_colors
+        bottom_materials = [
+            "cotton",
+            "linen",
+            "wool",
+            "denim",
+            "leather",
+            "corduroy",
+        ]
+        bottom_styles = [
+            "fluffy",
+            "slim-fit",
+            "baggy",
+            "wrinkled",
+            "ripped",
+            "distressed",
+            "cuffed",
+        ]
+
+        bottom_item = cls._choose(rng, bottom_items)
+        bottom_color = cls._choose(rng, bottom_colors)
+        bottom_material = cls._choose(rng, bottom_materials)
+        bottom_style = cls._choose(rng, bottom_styles)
+
+        bottom_tokens = [
+            f"{bottom_color} {bottom_item}",
+            f"{bottom_material} {bottom_item}",
+            f"{bottom_style} {bottom_item}",
+        ]
+
+        # --- Footwear (Category 10) --------------------------------------
+        # color + adjective + adjective
+        footwear_items = [
+            "boots",
+            "sneakers",
+            "shoes",
+            "sandals",
+            "loafers",
+            "ankle boots",
+        ]
+        footwear_colors = top_colors
+        footwear_styles = [
+            "ankle-high",
+            "knee-high",
+            "lace-up",
+            "slip-on",
+            "chunky sole",
+            "thin-heeled",
+            "low-top",
+            "high-top",
+        ]
+
+        footwear_item = cls._choose(rng, footwear_items)
+        footwear_color = cls._choose(rng, footwear_colors)
+        style1 = cls._choose(rng, footwear_styles)
+        remaining_styles = [s for s in footwear_styles if s != style1]
+        style2 = cls._choose(rng, remaining_styles)
+
+        footwear_tokens = [
+            f"{footwear_color} {footwear_item}",
+            f"{style1} {footwear_item}",
+            f"{style2} {footwear_item}",
+        ]
+
+        # --- Headwear (Category 11) --------------------------------------
+        # color + headwear + "headwear"
+        headwear_items = [
+            "baseball cap",
+            "knit beanie",
+            "beret",
+            "fedora",
+            "wide-brim hat",
+            "headband",
+            "hairband",
+            "tiara",
+            "hood",
+            "bandana",
+        ]
+        headwear_colors = top_colors
+
+        headwear_item = cls._choose(rng, headwear_items)
+        headwear_color = cls._choose(rng, headwear_colors)
+        headwear_token = f"{headwear_color} {headwear_item} headwear"
+
+        return {
+            "pov_tokens": pov_tokens,
+            "stage": stage,
+            "cat3_female": cat3_female,
+            "cat3_male": cat3_male,
+            "outer_trait": outer_trait,
+            "inner_trait": inner_trait,
+            "skin_token": skin_token,
+            "eye_color": eye_color,
+            "eye_style": eye_style,
+            "eye_direction": eye_direction,
+            "hair_color": hair_color,
+            "hair_length": hair_length,
+            "hair_style": hair_style,
+            "top_tokens": top_tokens,
+            "skirt_tokens": skirt_tokens,
+            "bottom_tokens": bottom_tokens,
+            "footwear_tokens": footwear_tokens,
+            "headwear_token": headwear_token,
+        }
+
+    # ---- Main generation -----------------------------------------------
+
+    def generate(self, seed: int):
+        rng = self._rng(seed)
+        concept = self._build_common_concept(rng)
+
+        # Category 1: fixed gender token (never anything else)
+        cat1_female = "1girl"
+        cat1_male = "1boy"
+
+        # Category 3: age / gender word
+        cat3_female = concept["cat3_female"]
+        cat3_male = concept["cat3_male"]
+
+        # Category 4: two tokens, second word identical within each prompt
+        # e.g. "rich woman, sadistic woman"
+        outer = concept["outer_trait"]
+        inner = concept["inner_trait"]
+
+        cat4_female = [
+            f"{outer} {cat3_female}",
+            f"{inner} {cat3_female}",
+        ]
+        cat4_male = [
+            f"{outer} {cat3_male}",
+            f"{inner} {cat3_male}",
+        ]
+
+        # Common tokens shared across both prompts
+        common_sequence = [
+            concept["skin_token"],
+            concept["eye_color"],
+            concept["eye_style"],
+            concept["eye_direction"],
+            concept["hair_color"],
+            concept["hair_length"],
+            concept["hair_style"],
+        ]
+        common_sequence.extend(concept["top_tokens"])
+        common_sequence.extend(concept["skirt_tokens"])
+        common_sequence.extend(concept["bottom_tokens"])
+        common_sequence.extend(concept["footwear_tokens"])
+        common_sequence.append(concept["headwear_token"])
+
+        # Build female token list
+        female_tokens = []
+        female_tokens.append(cat1_female)
+        female_tokens.extend(concept["pov_tokens"])
+        female_tokens.append(cat3_female)
+        female_tokens.extend(cat4_female)
+        female_tokens.extend(common_sequence)
+
+        # Build male token list
+        male_tokens = []
+        male_tokens.append(cat1_male)
+        male_tokens.extend(concept["pov_tokens"])
+        male_tokens.append(cat3_male)
+        male_tokens.extend(cat4_male)
+        male_tokens.extend(common_sequence)
+
+        female_prompt = ", ".join(female_tokens) + "."
+        male_prompt = ", ".join(male_tokens) + "."
+
+        return (female_prompt, male_prompt)
+
+
+NODE_CLASS_MAPPINGS = {
+    "Wildcard_3": Wildcard_3,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "Wildcard_3": "Wildcard_3",
+}