app.py

import spaces
import gradio as gr
import numpy as np
import random
import re
import torch
from diffusers import StableDiffusionXLPipeline
from huggingface_hub import list_repo_files

# ---- PATCH ----
import safetensors
from safetensors import safe_open as _orig_safe_open

class _SafeOpenWrapper:
    def __init__(self, f):
        self._f = f
    def __enter__(self):
        self._f.__enter__()
        return self
    def __exit__(self, *args):
        return self._f.__exit__(*args)
    def metadata(self):
        m = self._f.metadata()
        if not m or "format" not in m:
            return {"format": "pt"}
        return m
    def __getattr__(self, name):
        return getattr(self._f, name)

def _patched_safe_open(*args, **kwargs):
    return _SafeOpenWrapper(_orig_safe_open(*args, **kwargs))

safetensors.safe_open = _patched_safe_open
import transformers.modeling_utils
transformers.modeling_utils.safe_open = _patched_safe_open
# ---- PATCH LOPPU ----
# ...loput app.py:stä täsmälleen samana kuin edellisessä viestissä...

device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "kkntr/sdxl-kkntr"

pipe = StableDiffusionXLPipeline.from_pretrained(
    model_repo_id,
    torch_dtype=torch.float16,
    use_safetensors=True,
).to(device)

pipe.vae.enable_slicing()
pipe.enable_attention_slicing()

print("diffusers:", __import__('diffusers').__version__)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024

def apply_scratchpad(prompt):
    def replace(match):
        key = match.group(1).lower()
        if key in scratchpad:
            return random.choice(scratchpad[key])
        return match.group(0)
    return re.sub(r"\[([^\]]+)\]", replace, prompt)

scratchpad = {
    "animal": [
        "kangaroo", "kangaroo", "otter", "otter", "shark", "shark", "shark", "dolphin", "dolphin", "avian", "tiger", "lion", "wolf", "fox", "polar bear", "polar bear", "deer", "deer", "reindeer", "reindeer", "hyena", "bull", "bull", 
"rabbit", "panther", "moose", "cheetah", "badger", "striped hyena", "rat", "leopard", "thylacine", "bighorn sheep", 
"zebra", "horse", "horse", "donkey", "donkey", "unicorn", "white unicorn", "alligator", "western dragon", "lizard", "crocodile", "theropod",
"kangaroo", "otter", "tiger", "lion", "wolf", "fox", "polar bear", "deer", "reindeer", "hyena", "bull", 
"rabbit", "panther", "moose", "cheetah", "badger", "striped hyena", "rat", "leopard", "thylacine", "bighorn sheep", 
"zebra", "horse", "donkey", "unicorn", "white unicorn", "alligator", "western dragon", "lizard", "crocodile", "theropod",
"kangaroo", "otter", "tiger", "lion", "wolf", "fox", "polar bear", "deer", "reindeer", "hyena", "bull", 
"rabbit", "panther", "moose", "cheetah", "badger", "striped hyena", "rat", "leopard", "thylacine", "bighorn sheep", 
"zebra", "horse", "donkey", "unicorn", "white unicorn", "alligator", "western dragon", "lizard", "crocodile", "theropod",
"black russian terrier", "pit bull", "schnauzer", "dobermann", "mastiff", "molosser", "pinscher", "rottweiler", "tamaskan dog", 
"wolfdog", "alaskan husky", "alaskan malamute", "canadian eskimo dog", "german shepherd", "labrador husky", "siberian husky", 
"labrador", "golden retriever", "dalmatian", "border collie", "collie", "herding dog", "pastoral dog", "sheepdog", 
"carolina dog", "rhodesian ridgeback", "nordic sled dog", "spitz", "husky", "akita", "mountain dog", "primitive dog", 
"siberian retriever", "hunting dog", "australian cattle dog", "crash bandicoot", "montgomery gator, fnaf", "bear",
"parrot", "bison", "buffalo", "cattle", "muskox", "yak", "alpaca", "goat", "sheep", "deer", "elk", "moose", "reindeer", "white-tailed deer",
"raccoon", "skunk", "whale", "tasmanian devil", "badger", "ermine", "ferret", "marten", "mink", "otter", "weasel", "wolverine", "squirrel",
"white rabbit", "pegasus", "goat", "mouse", "cattle", "coyote", "owl", "charizard", "bandicoot", "giant panda", "panda", "ocelot", "panther",
"cheetah", "cougar", "puma", "pony", "camel", "llama", "penguin", "duck", "duck", "duck",
    ],

    "pose": [
        "flexing", "double biceps pose", "side chest pose", "front lat spread pose", "back double biceps pose", "side biceps pose",
"adonis pose", "arms akimbo", "crossed arms", "hands on hips", "hands behind head", "one hand behind head", "torso twist", "power stance",
"v-shape", "clenched fists", "palm open", "fist pump", "victory pose", "comic shrug", "superhero pose", "front double biceps pose",
"front lat spread pose", "side chest pose", "side triceps pose", "rear double biceps pose", "rear lat spread pose",
"most muscular pose", "abdominal and thigh pose", "hands-on hips pose",
    ],

    "swimwear": [
        "swimming trunks", "swim shorts", "swim briefs", "speedo", "boxer swim shorts", "bermuda swim shorts", "swimming thong",
"swim g-string", "board shorts", "jammers", "tight swim shorts", "loose swim shorts", "short swim trunks",
    ],

    "headwear": [
        "baseball cap", "backwards headwear", "backwards baseball cap", "snapback cap", "backwards snapback cap", "trucker hat", "backwards trucker hat",
"fitted cap", "beanie hat", "headband",
"bandana",
    ],

    "setting:private": [
        "bedroom", "bathroom", "shower", "bathtub"
"locker room", "changing room", "dressing room", 
"communal shower", "forest", "tent", "poolside",
"sauna", "hot tub", "jacuzzi", 
"gym locker room", "living room", "beach",
    ],

    "setting:semi": [
        "bedroom", "living room", "locker room", "beach", "tent", "beach", "gym", "fitness center", "weight room", "boxing gym", "martial arts dojo", 
"yoga studio", "crossfit box", "track and field stadium", "football field", "soccer pitch", "basketball court", "tennis court", "volleyball court", 
"basketball gym", "rugby field", "baseball field", "athletics track", "sports hall", "indoor sports arena", "climbing gym", "rowing machine area", 
"boxing ring", "wrestling mat", "exercise studio", "outdoor running track", "forest jogging trail", "park running path", "mountain trail", 
"soccer training field", "football practice field", "open field for sports", "stadium track", "indoor training facility", "cross-country track", 
"school sports field", "college sports field", "stadium gym", "athletic training ground", "exercise park", "outdoor gym area", "hiking trail with training stations",
    ],
    
    "setting:public": [
        "living room", "classroom", "bar", "street", "cafe", "restaurant", "mall", "supermarket", "bus stop", "train station", 
"subway station", "office", "library", "parking lot", "cinema", "arcade", "amusement park", "gas station", "bookstore", "hardware store", 
"coffee shop", "fast food restaurant", "pharmacy", "hotel lobby", "airport terminal", "shopping center", "convenience store", "train platform", 
"bus station", "school hallway", "university campus", "city square", "playground", "arcade room", "movie theater lobby", "food court", "bakery", 
"deli", "museum", "gallery", "ice cream shop", "pizzeria", "subway entrance", "train carriage", "bus interior", "train interior", "office lobby",
"conference room", "restaurant kitchen", "coffee bar", "shopping street", "nightclub", "bar", "club", "dance floor", "music festival",
"rooftop party", "late-night street party", "party", "summer party", "beach party", "dance club", "strip club", "rave club", "bar counter"
    ],

    "setting:party": [
        "nightclub", "bar", "club", "dance floor", "music festival",
"rooftop party", "late-night street party", "party", "summer party", "beach party", "dance club", "strip club", "rave club", "bar counter",
    ],
    
    "setting:water": [
        "beach", "seashore", "lake shore", "riverbank", "swimming pool", "private pool", "poolside",
"deck by the pool", "beach lounge", "hot tub", "jacuzzi",
    ],
    
    "setting:underwater": [
        "pool", "jacuzzi", "hot tub", "spa pool", "sea", "lake", "river",
    ],

    "hair": [
        "bald", "short hair", "medium hair", "long hair", "man bun", "ponytail", "crew cut", "undercut", "side part", "spiky hair", "messy hair",
"curly hair", "wavy hair", "straight hair",
    ],

    "accessories": [
        "earrings", "stud earrings", "diamond stud earrings", "hoop earrings", "small hoop earrings", "septum piercing", "eyebrow piercing",
"chain necklace", "beaded necklace", "pendant necklace", "sunglasses", "aviator sunglasses", "wayfarer sunglasses", "sports sunglasses",
"headphones", "earphones",
    ],

    "hairy:face": [
        "beard", "goatee", "soul patch", "sideburns", "stubble", "full beard", "short beard", "long beard", "chin strap", "van dyke", "mutton chops", "scruffy beard", "designer stubble",
    ],

    "hairy:body": [
        "chest hair", "armpit hair", "happy trail", "pubic hair",
"chest hair + armpit hair", "chest hair + happy trail", "chest hair + pubic hair", "armpit hair + happy trail", "armpit hair + pubic hair",
"happy trail + pubic hair", "chest hair + armpit hair + happy trail", "chest hair + armpit hair + pubic hair", "chest hair + happy trail + pubic hair",
"armpit hair + happy trail + pubic hair", "chest hair + armpit hair + happy trail + pubic hair",
    ],

    "body": [
        "very skinny", "lean", "fit", "toned", "athletic", "muscular", "broad-shouldered", "stocky", "solid", "heavily muscled", "chubby", "big and muscular", "obese",
    ],

    "underwear": [
        "boxer briefs", "briefs", "boxers", "trunks", "thong", "g-string", "string thong", "jockstrap", "low-rise briefs", "mid-rise briefs",
"high-rise briefs", "athletic briefs",
    ],

    "sex:anal": [
        "mating press", "chair position", "cowgirl position", "doggystyle", "leg glider position", "mastery position",
"Spoon Position", "reverse missionary position", "missionary position", "reverse cowgirl position",
"anvil Position", "guard Position", "stand and carry Position", "table lotus position"
    ],

    "sex:oral": [
        "69 position", "kneeling oral position", "sideways oral", "penis lick", "tongue in foreskin", "deepthroath", "tongue out blowjob", "irrumatio", 
"ball lick", "kneeling and blow position", "lying and blow position", "north pole position", "sit and blow position",
    ],

    "topwear:under": [
        "tank top", "tank top", "loose tank top, extended armholes, draped sleeveless shirt", "oversized muscle tank, baggy sleeveless top, open armholes",
"t-shirt", "crew-neck t-shirt", "v-neck t-shirt", "sleeveless shirt", "cotton undershirt",
    ],

    "topwear:casual": [
        "long-sleeve t-shirt", "crew-neck long-sleeve shirt", "v-neck long-sleeve shirt", "henley long-sleeve", "hoodie",
"sweatshirt", "zip-up hoodie", "pullover sweater", "thermal shirt", "flannel shirt",
    ],

    "topwear:formal": [
        "button-up long-sleeve shirt", "collared shirt", "dress shirt", "oxford shirt", "polo shirt", "unbuttoned shirt, flannel shirt,"
    ],

    "topwear:party": [
        "mesh tank top", "mesh shirt", "harness", "cage top", "leather vest", "rubber shirt", "tight tank top", "open shirt", "crop top",
"fishnet top", "bondage harness", "strappy top", "latex shirt", "transparent shirt", "studded vest", "festival top", "topless", "topless",
    ],
    
    "bottomwear:formal": [
        "jeans", "chinos", "khaki pants", "linen trousers", "cotton trousers", "dress trousers", "slacks", "wool trousers", "formal pants",
    ],

    "bottomwear:casual": [
        "joggers", "sweatpants", "track pants", "gym shorts", "shorts", "linen shorts", "bermuda shorts", "cargo pants", "basketball shorts", "training pants",
    ],

    "bottomwear:party": [
        "camo pants", "leather pants", "rubber pants", "cargo pants", "denim pants", "tight jeans", "distressed jeans", "ripped jeans", "patent leather pants",
"bondage pants", "festival trousers", "shiny pants", "studded pants", "paint-splatter pants", "metallic pants", "faux leather pants", "tight cargo pants",
"plaid pants",
    ],

    "light": [
        "morning", "late morning", "noon", "early afternoon", "afternoon", "late afternoon", "evening", "dusk", "twilight", "sunrise", "sunset", 
"day", "midday sun", "night", "midnight", "pre-dawn", "overcast", "cloudy", "foggy", "misty", "hazy", "dim light", "soft light", "ambient light", 
"natural light", "diffused light", "soft daylight", "warm light", "cool light", "golden hour", "blue hour", "soft morning light", "soft evening light", 
"fading light", "gentle light", "muted light", "soft shadows", "soft glow", 
    ],

    "perspective": [
        "slightly low angle", "slightly high angle", "subtle birds-eye view", "gentle worm's-eye view", "slightly tilted", "soft dutch angle", 
"subtle foreshortening", "soft overhead view", "slight top-down view", "gentle angled view", "slight perspective shift", "slightly slanted composition", 
"soft three-quarter view", "slightly off-center view", "soft angled composition", "slightly tilted horizon", "subtle dynamic angle", "soft wide angle", 
"centered composition", "neutral angle", "straight-on view", "slight zoom-in", "slight zoom-out", "slightly raised angle", "slightly lowered angle", 
"gentle framing", "balanced composition", "softly framed subject", "slightly front-on", "slightly side-on", "soft mid-shot angle", "gentle medium shot", 
"slightly elevated viewpoint", "slightly lowered viewpoint",
    ],

    "effect": [
        "grainy", "bokeh", "soft focus", "motion blur", "depth of field", "slight vignette", "subtle glow", "diffused light", "dusty", 
"hazy", "slight fog", "light haze", "soft shadows", "backlight glow", "atmospheric haze", "slight bloom", "color gradient", "warm tone", 
"cool tone", "slight desaturation", "light mist", "soft contrast", "light diffusion", "soft highlight", "subtle reflection", "ambient occlusion", 
"soft rim light", "light scattering", "faded edges", "gentle flare", "soft vignette", "soft bokeh", "blurred background", "slight overexposure", 
"slight underexposure", "faint glow", "soft illumination", "dust particles", "floating dust", "light specks", "tiny particles", "airborne dust", 
"dust motes", "light haze with particles", "soft smoke", "pollen particles", "floating motes", "misty particles", "ambient dust", "soft airborne light particles",
    ],

    "face": [
        "flirty", "seductive", "playful flirt", "teasing look", "sultry gaze", "wink flirt", "one eye closed", "smoldering look", "cheeky flirt", "suggestive grin",
"mischievous smirk", "playful smirk", "tongue out flirty", "open mouth flirt", "eye contact flirt", "raised eyebrow flirt", "smiling", "grinning", "laughing", "happy",
"joyful", "playful", "quirky", "cheerful", "friendly smile", "tongue out", "winking", "raised eyebrows", "blushing", "embarrassed", "shy", "bashful", "surprised",
"curious", "amused", "satisfied smile", "soft smile", "teasing smile", "cheeky grin", "slight smirk", "mischievous grin", "open mouth smile", "excited", "delighted",
"flirty", "seductive", "playful flirt", "teasing look", "sultry gaze", "wink flirt",
    ],

    "color": [
        "red", "scarlet", "crimson", "pink", "magenta", "fuchsia", "orange", "tangerine", "yellow", "bright yellow", "lemon yellow color", "gold color", "green", "lime green color",
"emerald", "olive", "teal", "turquoise", "cyan", "blue", "sky blue", "royal blue", "navy blue", "indigo", "purple", "violet", "lavender", "lilac", "brown", "tan",
"beige", "bronze", "maroon", "burgundy", "white", "ivory", "black", "grey", "dark-grey", "light-grey", "silver", "reflecting chrome", "copper", "mint", "jade", 
"peach color", "camo", "camouflage", "red-violet", "blue-green", "turquoise-blue", "yellow-orange", "pink-purple", "mustard color",
    ],
}

QUALITY_SUFFIX = ", best quality, masterpiece, ultra detailed, sharp focus, perfect anatomy, 8k"

STYLE_LORAS = [
    {"repo": "kkntr/lora14", "name": "bakemonoy", "weight": 0.3},
    {"repo": "kkntr/pre", "name": "takahiro", "weight": 0.5},
    {"repo": "kkntr/hypermuscles", "name": "exfore", "weight": 0.1},
]

def load_any_lora(pipe, repo, adapter_name):
    files = list_repo_files(repo)
    weight = next(f for f in files if f.endswith(".safetensors"))
    pipe.load_lora_weights(repo, weight_name=weight, adapter_name=adapter_name)
    return True

adapter_names = []
adapter_weights = []

for lora in STYLE_LORAS:
    try:
        load_any_lora(pipe, lora["repo"], lora["name"])
        adapter_names.append(lora["name"])
        adapter_weights.append(lora["weight"])
        print(f"Loaded LoRA: {lora['repo']}")
    except Exception as e:
        print(f"Failed LoRA {lora['repo']}: {e}")

if adapter_names:
    pipe.set_adapters(adapter_names, adapter_weights=adapter_weights)

@spaces.GPU
def infer(prompt, negative_prompt, seed, randomize_seed, random_resolution, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    generator = torch.Generator(device=device).manual_seed(seed)
    if random_resolution:
        resolutions = [(592,1768),(608,1696),(640,1624),(672,1544),(720,1440),(744,1392),(784,1320),(832,1248),(864,1208),(880,1184),(912,1144),(976,1072),(1024,1024),(1072,976),(1144,912),(1184,880),(1208,864),(1248,832),(1320,784),(1392,744),(1440,720),(1544,672),(1624,640),(1696,608),(1768,592)]
        width, height = random.choice(resolutions)
    final_prompt = apply_scratchpad(prompt) + QUALITY_SUFFIX
    image = pipe(prompt=final_prompt, negative_prompt=negative_prompt, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, width=width, height=height, generator=generator).images[0]
    return image, seed, final_prompt

examples = ["[animal], [body], [sex:anal/oral], [pose], [hairy:face/body], [topwear:under/casual/formal/party], [bottomwear:casual/formal/party], [underwear], [swimwear], [headwear], [hair], [accessories], [face], [light], [perspective], [effect], [setting:private/semi/public/party/water/underwater], [color]"]
["[animal], [body], [color] [bottomwear:casual], [topwear:casual]"]

css = "#col-container {margin: 0 auto; max-width: 640px;}"

with gr.Blocks() as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown(" # Text-to-Image Gradio Template")
        with gr.Row():
            prompt = gr.Textbox(
                label="Prompt", 
                show_label=False, 
                lines=4,              # <— tämä tekee siitä ison
                max_lines=10,         # käyttäjä voi kasvattaa
                placeholder="Enter your prompt", 
                container=True,       # näyttää nätimmän laatikon
                scale=4
            )
            run_button = gr.Button("Run", scale=0, variant="primary")
        result = gr.Image(label="Result", show_label=False)
        used_prompt = gr.Textbox(label="Used Prompt", interactive=False, lines=4)
        with gr.Accordion("Advanced Settings", open=False):
            negative_prompt = gr.Textbox(label="Negative Prompt", lines=4, value="worst quality, low quality, lowres, blurry, pixelated, jpeg artifacts, bad anatomy, bad hands, extra fingers, missing fingers, malformed hands, deformed, mutated, poorly drawn face, poorly drawn eyes, cross-eyed, duplicate, extra limbs, cropped, watermark, text, logo, signature, username, out of frame")
            seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
            with gr.Row():
                random_resolution = gr.Checkbox(label="Random resolution", value=True)
                width = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
                height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
            with gr.Row():
                guidance_scale = gr.Slider(label="Guidance scale", minimum=0.0, maximum=10.0, step=0.1, value=7.5)
                num_inference_steps = gr.Slider(label="Number of inference steps", minimum=1, maximum=50, step=1, value=20)
        gr.Examples(examples=examples, inputs=[prompt])
    gr.on(triggers=[run_button.click, prompt.submit], fn=infer, inputs=[prompt, negative_prompt, seed, randomize_seed, random_resolution, width, height, guidance_scale, num_inference_steps], outputs=[result, seed, used_prompt])

if __name__ == "__main__":
    demo.launch(css=css, ssr_mode=False)