app.py

import gradio as gr
import numpy as np
import random
import torch
import spaces
from diffusers import DiffusionPipeline

from tags_straight import TAGS_STRAIGHT
from tags_lesbian import TAGS_LESBIAN
from tags_gay import TAGS_GAY

PROMPT_PREFIXES = {
    "Prompt Input": "score_9, score_8_up, score_7_up, source_anime",
    "Straight": "score_9, score_8_up, score_7_up, source_anime, ",
    "Lesbian": "score_9, score_8_up, score_7_up, source_anime, ",
    "Gay": "score_9, score_8_up, score_7_up, source_anime, yaoi, "
}

device = "cuda" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if device == "cuda" else torch.float32



# model_repo_id = "John6666/wai-ani-nsfw-ponyxl-v8-sdxl"
model_repo_id = "John6666/wai-ani-nsfw-ponyxl-v140-sdxl"
# model_repo_id = "John6666/pony-realism-v23-ultra-sdxl"

pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype).to(device)

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

def create_checkboxes(tag_dict, suffix):
    categories = list(tag_dict.keys())
    return [gr.CheckboxGroup(choices=list(tag_dict[cat].keys()), label=f"{cat} Tags ({suffix})") for cat in categories], categories

straight_checkboxes, _ = create_checkboxes(TAGS_STRAIGHT, "Straight")
lesbian_checkboxes, _ = create_checkboxes(TAGS_LESBIAN, "Lesbian")
gay_checkboxes, _ = create_checkboxes(TAGS_GAY, "Gay")

@spaces.GPU
def infer(prompt, negative_prompt, seed, randomize_seed, width, height,
          guidance_scale, num_inference_steps, active_tab, *tag_selections,
          progress=gr.Progress(track_tqdm=True)):

    prefix = PROMPT_PREFIXES.get(active_tab, "score_9, score_8_up, score_7_up, source_anime")

    if active_tab == "Prompt Input":
        final_prompt = f"{prefix}, {prompt}"
    else:
        combined_tags = []

        straight_len = len(TAGS_STRAIGHT)
        lesbian_len = len(TAGS_LESBIAN)
        gay_len = len(TAGS_GAY)

        if active_tab == "Straight":
            for (tag_name, tag_dict), selected in zip(TAGS_STRAIGHT.items(), tag_selections[:straight_len]):
                combined_tags.extend([tag_dict[tag] for tag in selected])
        elif active_tab == "Lesbian":
            offset = straight_len
            for (tag_name, tag_dict), selected in zip(TAGS_LESBIAN.items(), tag_selections[offset:offset+lesbian_len]):
                combined_tags.extend([tag_dict[tag] for tag in selected])
        elif active_tab == "Gay":
            offset = straight_len + lesbian_len
            for (tag_name, tag_dict), selected in zip(TAGS_GAY.items(), tag_selections[offset:offset+gay_len]):
                combined_tags.extend([tag_dict[tag] for tag in selected])

        tag_string = ", ".join(combined_tags)
        final_prompt = f"{prefix} {tag_string}"

    negative_base = "worst quality, bad quality, jpeg artifacts, source_cartoon, 3d, (censor), monochrome, blurry, lowres, watermark"
    full_negative_prompt = f"{negative_base}, {negative_prompt}"

    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    generator = torch.Generator().manual_seed(seed)

    image = pipe(
        prompt=final_prompt,
        negative_prompt=full_negative_prompt,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        width=width,
        height=height,
        generator=generator
    ).images[0]

    return image, seed, f"Prompt used: {final_prompt}\nNegative prompt used: {full_negative_prompt}"


css = """
#col-container {
    margin: 0 auto;
    max-width: 1280px;
}

#left-column {
    width: 50%;
    display: inline-block;
    padding: 20px;
    vertical-align: top;
}

#right-column {
    width: 50%;
    display: inline-block;
    vertical-align: top;
    padding: 20px;
    margin-top: 53px;
}

#run-button {
    width: 100%;
    margin-top: 10px;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Row():
        with gr.Column(elem_id="left-column"):
            gr.Markdown("# Rainbow Media X")

            result = gr.Image(label="Result", show_label=False)
            prompt_info = gr.Textbox(label="Prompts Used", lines=3, interactive=False)

            with gr.Accordion("Advanced Settings", open=False):
                negative_prompt = gr.Textbox(label="Negative prompt", placeholder="Enter negative prompt")
                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():
                    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, maximum=10, step=0.1, value=7)
                    num_inference_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=50, step=1, value=35)

            run_button = gr.Button("Run", elem_id="run-button")

        with gr.Column(elem_id="right-column"):
            active_tab = gr.State("Prompt Input")

            with gr.Tabs() as tabs:
                with gr.TabItem("Prompt Input") as prompt_tab:
                    prompt = gr.Textbox(label="Prompt", lines=3, placeholder="Enter your prompt")
                    prompt_tab.select(lambda: "Prompt Input", outputs=active_tab)

                with gr.TabItem("Straight") as straight_tab:
                    for cb in straight_checkboxes:
                        cb.render()
                    straight_tab.select(lambda: "Straight", outputs=active_tab)

                with gr.TabItem("Lesbian") as lesbian_tab:
                    for cb in lesbian_checkboxes:
                        cb.render()
                    lesbian_tab.select(lambda: "Lesbian", outputs=active_tab)

                with gr.TabItem("Gay") as gay_tab:
                    for cb in gay_checkboxes:
                        cb.render()
                    gay_tab.select(lambda: "Gay", outputs=active_tab)

    run_button.click(
        fn=infer,
        inputs=[
            prompt, negative_prompt, seed, randomize_seed,
            width, height, guidance_scale, num_inference_steps,
            active_tab,
            *straight_checkboxes,
            *lesbian_checkboxes,
            *gay_checkboxes
        ],
        outputs=[result, seed, prompt_info]
    )

demo.queue().launch()