app.py

import spaces
import gradio as gr
import numpy as np
import PIL.Image
from PIL import Image
import random
from diffusers import StableDiffusionXLPipeline
from diffusers import EulerAncestralDiscreteScheduler
import torch
from compel import Compel, ReturnedEmbeddingsType

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

# Make sure to use torch.float16 consistently throughout the pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "votepurchase/pornmasterPro_noobV3VAE",
    torch_dtype=torch.float16,
    variant="fp16",  # Explicitly use fp16 variant
    use_safetensors=True  # Use safetensors if available
)

pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to(device)

# Force all components to use the same dtype
pipe.text_encoder.to(torch.float16)
pipe.text_encoder_2.to(torch.float16)
pipe.vae.to(torch.float16)
pipe.unet.to(torch.float16)

# 追加: Initialize Compel for long prompt processing
compel = Compel(
    tokenizer=[pipe.tokenizer, pipe.tokenizer_2],
    text_encoder=[pipe.text_encoder, pipe.text_encoder_2],
    returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
    requires_pooled=[False, True],
    truncate_long_prompts=False
)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1216

# Default prompt
DEFAULT_PROMPT = "Detailed illustration, realistic style, portrait of a beautiful Japanese woman, wearing an elegant traditional Japanese uniform, neatly tailored with intricate patterns and subtle textures, serene expression, soft natural lighting, standing gracefully in a traditional Japanese garden with cherry blossom petals gently falling in the background, cinematic quality, ultra-detailed, high-resolution, warm tones"

# 追加: Simple long prompt processing function
def process_long_prompt(prompt, negative_prompt=""):
    """Simple long prompt processing using Compel"""
    try:
        conditioning, pooled = compel([prompt, negative_prompt])
        return conditioning, pooled
    except Exception as e:
        print(f"Long prompt processing failed: {e}, falling back to standard processing")
        return None, None
    
@spaces.GPU
def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
    # 変更: Remove the 60-word limit warning and add long prompt check
    use_long_prompt = len(prompt.split()) > 60 or len(prompt) > 300
    
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    generator = torch.Generator(device=device).manual_seed(seed)
    
    try:
        # 追加: Try long prompt processing first if prompt is long
        if use_long_prompt:
            print("Using long prompt processing...")
            conditioning, pooled = process_long_prompt(prompt, negative_prompt)
            
            if conditioning is not None:
                output_image = pipe(
                    prompt_embeds=conditioning[0:1],
                    pooled_prompt_embeds=pooled[0:1],
                    negative_prompt_embeds=conditioning[1:2],
                    negative_pooled_prompt_embeds=pooled[1:2],
                    guidance_scale=guidance_scale,
                    num_inference_steps=num_inference_steps,
                    width=width,
                    height=height,
                    generator=generator
                ).images[0]
                return output_image
        
        # Fall back to standard processing
        output_image = pipe(
            prompt=prompt,
            negative_prompt=negative_prompt,
            guidance_scale=guidance_scale,
            num_inference_steps=num_inference_steps,
            width=width,
            height=height,
            generator=generator
        ).images[0]
        
        return output_image
    except RuntimeError as e:
        print(f"Error during generation: {e}")
        # Return a blank image with error message
        error_img = Image.new('RGB', (width, height), color=(0, 0, 0))
        return error_img


css = """
/* Main container styling */
#col-container {
    margin: 0 auto;
    max-width: 520px;
}

/* Gradient background for the entire app */
.gradio-container {
    background: linear-gradient(135deg, #667eea 0%, #764ba2 25%, #f093fb 50%, #f5576c 75%, #ffc947 100%);
    min-height: 100vh;
}

/* Main block styling with semi-transparent background */
.contain {
    background: rgba(255, 255, 255, 0.95);
    border-radius: 20px;
    padding: 20px;
    box-shadow: 0 8px 32px 0 rgba(31, 38, 135, 0.37);
    backdrop-filter: blur(4px);
    border: 1px solid rgba(255, 255, 255, 0.18);
}

/* Input field styling */
.gr-text-input {
    background: rgba(255, 255, 255, 0.9) !important;
    border: 2px solid rgba(102, 126, 234, 0.3) !important;
    border-radius: 10px !important;
}

/* Button styling */
.gr-button {
    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important;
    border: none !important;
    color: white !important;
    font-weight: bold !important;
    transition: all 0.3s ease !important;
}

.gr-button:hover {
    transform: translateY(-2px);
    box-shadow: 0 5px 15px rgba(102, 126, 234, 0.4);
}

/* Accordion styling */
.gr-accordion {
    background: rgba(255, 255, 255, 0.8) !important;
    border-radius: 10px !important;
    margin-top: 10px !important;
}

/* Result image container */
.gr-image {
    border-radius: 15px !important;
    box-shadow: 0 4px 15px rgba(0, 0, 0, 0.1) !important;
}

/* Slider styling */
.gr-slider {
    background: rgba(255, 255, 255, 0.8) !important;
}
"""

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

    with gr.Column(elem_id="col-container"):
        gr.Markdown(
            """
            # 🎨 Stable Diffusion XL Image Generator
            ### Create beautiful images with AI
            """
        )

        # Badge section
        gr.HTML(
            """
            <div style="display: flex; justify-content: center; align-items: center; gap: 20px; margin: 20px 0;">
                <a href="https://huggingface.co/spaces/Heartsync/Wan-2.2-ADULT" target="_blank">
                    <img src="https://img.shields.io/static/v1?label=T2I%20%26%20TI2V&message=Wan-2.2-ADULT&color=%230000ff&labelColor=%23800080&logo=huggingface&logoColor=white&style=for-the-badge" alt="badge">
                </a>
                <a href="https://huggingface.co/spaces/Heartsync/PornHUB" target="_blank">
                    <img src="https://img.shields.io/static/v1?label=T2I%20&message=PornHUB&color=%230000ff&labelColor=%23800080&logo=huggingface&logoColor=white&style=for-the-badge" alt="badge">
                </a>
                <a href="https://huggingface.co/spaces/Heartsync/Hentai-Adult" target="_blank">
                    <img src="https://img.shields.io/static/v1?label=T2I%20&message=Hentai-Adult&color=%230000ff&labelColor=%23800080&logo=huggingface&logoColor=white&style=for-the-badge" alt="badge">
                </a>
            </div>
            """
        )
        
        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt (long prompts are automatically supported)",
                container=False,
                value=DEFAULT_PROMPT  # Set default prompt
            )

            run_button = gr.Button("Run", scale=0)

        result = gr.Image(label="Result", show_label=False)
        
        with gr.Accordion("Advanced Settings", open=False):

            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                value="nsfw, (low quality, worst quality:1.2), very displeasing, 3d, watermark, signature, ugly, poorly drawn"
            )

            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=20.0,
                    step=0.1,
                    value=7,
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=28,
                    step=1,
                    value=28,
                )

    run_button.click(
        fn=infer,
        inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
        outputs=[result]
    )

demo.queue().launch()