app.py

import gradio as gr
import numpy as np
import random
import spaces  # [uncomment to use ZeroGPU]
from diffusers import DiffusionPipeline, DPMSolverSDEScheduler
import torch
from tags import participant_tags, tribe_tags, skin_tone_tags, body_type_tags, tattoo_tags, piercing_tags, expression_tags, eye_tags, hair_style_tags, position_tags, fetish_tags, location_tags, camera_tags, atmosphere_tags  # Import tags here

device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "John6666/wai-ani-nsfw-ponyxl-v8-sdxl"  # Replace with your desired model

if torch.cuda.is_available():
    torch_dtype = torch.float16
else:
    torch_dtype = torch.float32

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

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

@spaces.GPU  # [uncomment to use ZeroGPU]
def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps,
          selected_participant_tags, selected_tribe_tags, selected_skin_tone_tags, selected_body_type_tags, 
          selected_tattoo_tags, selected_piercing_tags, selected_expression_tags, selected_eye_tags, 
          selected_hair_style_tags, selected_position_tags, selected_fetish_tags, selected_location_tags, 
          selected_camera_tags, selected_atmosphere_tags, use_tags, progress=gr.Progress(track_tqdm=True)):

    # Construct final prompt using selected tags if `use_tags` is True
    if use_tags:
        selected_tags = (
            [participant_tags[tag] for tag in selected_participant_tags] +
            [tribe_tags[tag] for tag in selected_tribe_tags] +
            [skin_tone_tags[tag] for tag in selected_skin_tone_tags] +
            [body_type_tags[tag] for tag in selected_body_type_tags] +
            [tattoo_tags[tag] for tag in selected_tattoo_tags] +
            [piercing_tags[tag] for tag in selected_piercing_tags] +
            [expression_tags[tag] for tag in selected_expression_tags] +
            [eye_tags[tag] for tag in selected_eye_tags] +
            [hair_style_tags[tag] for tag in selected_hair_style_tags] +
            [position_tags[tag] for tag in selected_position_tags] +
            [fetish_tags[tag] for tag in selected_fetish_tags] +
            [location_tags[tag] for tag in selected_location_tags] +
            [camera_tags[tag] for tag in selected_camera_tags] +
            [atmosphere_tags[tag] for tag in selected_atmosphere_tags]
        )
        tags_text = ', '.join(selected_tags)
        final_prompt = f'score_9, score_8_up, score_7_up, source_anime, {tags_text}'
    else:
        final_prompt = f'score_9, score_8_up, score_7_up, source_anime, {prompt}'

    # Concatenate user-provided negative prompt with additional restrictions
    additional_negatives = "worst quality, bad quality, jpeg artifacts, source_cartoon, 3d, (censor), monochrome, blurry, lowres, watermark"
    full_negative_prompt = f"{additional_negatives}, {negative_prompt}"

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

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

    # Generate the image with the final prompts
    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, and the used prompts
    return image, seed, f"Prompt used: {final_prompt}\nNegative prompt used: {full_negative_prompt}"


examples = [
    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
    "An astronaut riding a green horse",
    "A delicious ceviche cheesecake slice",
]

css = """
#col-container {
    margin: 0 auto;
    max-width: 640px;
}
#run-button {
    width: 100%;
}
"""

with gr.Blocks(css=css) as demo:

    with gr.Column(elem_id="col-container"):
        gr.Markdown("""# Text-to-Image Gradio Template""")

        # Display result image at the top
        result = gr.Image(label="Result", show_label=False)

        # Add a textbox to display the prompts used for generation
        prompt_info = gr.Textbox(label="Prompts Used", lines=3, interactive=False)

        # Tabbed interface to select either Prompt or Tags
        with gr.Tabs() as tabs:
            with gr.TabItem("Prompt Input"):
                prompt = gr.Textbox(
                    label="Prompt",
                    show_label=False,
                    max_lines=1,
                    placeholder="Enter your prompt",
                    container=False,
                )
                use_tags = gr.State(False)

            with gr.TabItem("Tag Selection"):
                # Tag selection checkboxes for each tag group
                selected_participant_tags = gr.CheckboxGroup(choices=list(participant_tags.keys()), label="Participant Tags")
                selected_tribe_tags = gr.CheckboxGroup(choices=list(tribe_tags.keys()), label="Tribe Tags")
                selected_skin_tone_tags = gr.CheckboxGroup(choices=list(skin_tone_tags.keys()), label="Skin Tone Tags")
                selected_body_type_tags = gr.CheckboxGroup(choices=list(body_type_tags.keys()), label="Body Type Tags")
                selected_tattoo_tags = gr.CheckboxGroup(choices=list(tattoo_tags.keys()), label="Tattoo Tags")
                selected_piercing_tags = gr.CheckboxGroup(choices=list(piercing_tags.keys()), label="Piercing Tags")
                selected_expression_tags = gr.CheckboxGroup(choices=list(expression_tags.keys()), label="Expression Tags")
                selected_eye_tags = gr.CheckboxGroup(choices=list(eye_tags.keys()), label="Eye Tags")
                selected_hair_style_tags = gr.CheckboxGroup(choices=list(hair_style_tags.keys()), label="Hair Style Tags")
                selected_position_tags = gr.CheckboxGroup(choices=list(position_tags.keys()), label="Position Tags")
                selected_fetish_tags = gr.CheckboxGroup(choices=list(fetish_tags.keys()), label="Fetish Tags")
                selected_location_tags = gr.CheckboxGroup(choices=list(location_tags.keys()), label="Location Tags")
                selected_camera_tags = gr.CheckboxGroup(choices=list(camera_tags.keys()), label="Camera Tags")
                selected_atmosphere_tags = gr.CheckboxGroup(choices=list(atmosphere_tags.keys()), label="Atmosphere Tags")

                use_tags = gr.State(True)

        # Full-width "Run" button
        run_button = gr.Button("Run", scale=0, elem_id="run-button")

        with gr.Accordion("Advanced Settings", open=False):
            negative_prompt = gr.Textbox(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                visible=True,
            )

            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=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=7,
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=35,
                )

        gr.Examples(
            examples=examples,
            inputs=[prompt]
        )

        run_button.click(
            infer,
            inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, 
                    selected_participant_tags, selected_tribe_tags, selected_skin_tone_tags, selected_body_type_tags, 
                    selected_tattoo_tags, selected_piercing_tags, selected_expression_tags, selected_eye_tags, 
                    selected_hair_style_tags, selected_position_tags, selected_fetish_tags, selected_location_tags, 
                    selected_camera_tags, selected_atmosphere_tags, use_tags],
            outputs=[result, seed, prompt_info]
        )

demo.queue().launch()