app.py

import gradio as gr
import numpy as np
import random

import spaces  # [uncomment to use ZeroGPU]
from diffusers import DiffusionPipeline
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "John6666/wai-ani-nsfw-ponyxl-v8-sdxl"  # Replace to the model you would like to use

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

# Specific prefixes for the prompt and negative prompt
prompt_prefix = "score_9, score_8_up, score_7_up, source_anime"
negative_prompt_prefix = "score_6, score_5, score_4, source_cartoon, 3d, (censor), monochrome, blurry, lowres, watermark"

@spaces.GPU  # [uncomment to use ZeroGPU]
def infer(
    prompt,
    negative_prompt,
    seed,
    randomize_seed,
    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().manual_seed(seed)

    full_prompt = f"{prompt_prefix}, {prompt}"
    full_negative_prompt = f"{negative_prompt_prefix}, {negative_prompt}"

    image = pipe(
        prompt=full_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, full_prompt, full_negative_prompt

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

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown(" # Rainbow Media Anime Generator")
        gr.Markdown(' ### <a href="https://huggingface.co/spaces/panelforge/rainbow-media-real-v11" target="_blank" class="button-link">Try a more realistic model</a>') # Update links

        result = gr.Image(label="Result", show_label=False)

        prompt = gr.Text(
            label="Prompt",
            lines=3,
            placeholder="Enter your prompt",
            container=False,
        )

        negative_prompt = gr.Text(
            label="Negative prompt",
            lines=3,
            placeholder="Enter a negative prompt",
            container=False,
        )

        run_button = gr.Button("Run", variant="primary")

        with gr.Accordion("Advanced Settings", open=False):              

            with gr.Row():
                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,  # Replace with defaults that work for your model
                )

                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,  # Replace with defaults that work for your model
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=7.0,  # Replace with defaults that work for your model
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=35,  # Replace with defaults that work for your model
                )

            # Add text outputs to show full prompt and negative prompt
            full_prompt_output = gr.Textbox(label="Full Prompt", interactive=False, lines=3)
            full_negative_prompt_output = gr.Textbox(label="Full Negative Prompt", interactive=False, lines=3)

    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            prompt,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
        ],
        outputs=[result, seed, full_prompt_output, full_negative_prompt_output],
    )

if __name__ == "__main__":
    demo.launch()