app.py

import os
import gc
import gradio as gr
import numpy as np
import torch
import json
import spaces
import config
import utils
import logging
from PIL import Image, PngImagePlugin
from datetime import datetime
from diffusers.models import AutoencoderKL
from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

DESCRIPTION = "PonyDiffusion V6 XL"
if not torch.cuda.is_available():
    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU. </p>"
IS_COLAB = utils.is_google_colab() or os.getenv("IS_COLAB") == "1"
HF_TOKEN = os.getenv("HF_TOKEN")
CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1"
MIN_IMAGE_SIZE = int(os.getenv("MIN_IMAGE_SIZE", "512"))
MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "2048"))
USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1"
ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1"
OUTPUT_DIR = os.getenv("OUTPUT_DIR", "./outputs")
THUMBNAIL_SIZE = (128, 128)  # Size for thumbnails

MODEL = os.getenv(
    "MODEL",
    "https://huggingface.co/AstraliteHeart/pony-diffusion-v6/blob/main/v6.safetensors",
)

torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# Store the generation history
generation_history = []

def load_pipeline(model_name):
    # ... (rest of the function remains the same)

@spaces.GPU
def generate(
    prompt: str,
    negative_prompt: str = "",
    seed: int = 0,
    custom_width: int = 1024,
    custom_height: int = 1024,
    guidance_scale: float = 7.0,
    num_inference_steps: int = 30,
    sampler: str = "DPM++ 2M SDE Karras",
    aspect_ratio_selector: str = "1024 x 1024",
    use_upscaler: bool = False,
    upscaler_strength: float = 0.55,
    upscale_by: float = 1.5,
    progress=gr.Progress(track_tqdm=True),
) -> Image:
    # ... (rest of the function remains the same)

    try:
        # ... (existing code for image generation)

        if images:
            # Create thumbnail
            thumbnail = images[0].copy()
            thumbnail.thumbnail(THUMBNAIL_SIZE)
            
            # Add to generation history
            generation_history.append({
                "prompt": prompt,
                "thumbnail": thumbnail,
                "metadata": metadata
            })

            if IS_COLAB:
                for image in images:
                    filepath = utils.save_image(image, metadata, OUTPUT_DIR)
                    logger.info(f"Image saved as {filepath} with metadata")

        return images, metadata, update_history()
    except Exception as e:
        logger.exception(f"An error occurred: {e}")
        raise
    finally:
        if use_upscaler:
            del upscaler_pipe
        pipe.scheduler = backup_scheduler
        utils.free_memory()

def update_history():
    history_html = "<div style='display: flex; flex-wrap: wrap;'>"
    for item in reversed(generation_history[-10:]):  # Show last 10 entries
        thumbnail_path = f"data:image/png;base64,{utils.image_to_base64(item['thumbnail'])}"
        history_html += f"""
        <div style='margin: 5px; text-align: center;'>
            <img src='{thumbnail_path}' style='width: 100px; height: 100px; object-fit: cover;'>
            <p style='font-size: 12px; margin: 5px 0;'>{item['prompt'][:50]}...</p>
        </div>
        """
    history_html += "</div>"
    return history_html

if torch.cuda.is_available():
    pipe = load_pipeline(MODEL)
    logger.info("Loaded on Device!")
else:
    pipe = None

with gr.Blocks(css="style.css") as demo:
    title = gr.HTML(
        f"""<h1><span>{DESCRIPTION}</span></h1>""",
        elem_id="title",
    )
    gr.Markdown(
        f"""Gradio demo for ([Pony Diffusion V6]https://civitai.com/models/257749/pony-diffusion-v6-xl/)""",
        elem_id="subtitle",
    )
    gr.DuplicateButton(
        value="Duplicate Space for private use",
        elem_id="duplicate-button",
        visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
    )
    with gr.Group():
        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=5,
                placeholder="Enter your prompt",
                container=False,
            )
            run_button = gr.Button(
                "Generate", 
                variant="primary", 
                scale=0
            )
        result = gr.Gallery(
            label="Result", 
            columns=1, 
            preview=True, 
            show_label=False
        )
    
    # Add the history display
    history_display = gr.HTML(label="Generation History")

    with gr.Accordion(label="Advanced Settings", open=False):
        # ... (rest of the UI components remain the same)

    with gr.Accordion(label="Generation Parameters", open=False):
        gr_metadata = gr.JSON(label="Metadata", show_label=False)

    gr.Examples(
        examples=config.examples,
        inputs=prompt,
        outputs=[result, gr_metadata, history_display],
        fn=lambda *args, **kwargs: generate(*args, use_upscaler=True, **kwargs),
        cache_examples=CACHE_EXAMPLES,
    )

    # ... (rest of the event handlers remain the same)

    inputs = [
        prompt,
        negative_prompt,
        seed,
        custom_width,
        custom_height,
        guidance_scale,
        num_inference_steps,
        sampler,
        aspect_ratio_selector,
        use_upscaler,
        upscaler_strength,
        upscale_by,
    ]

    prompt.submit(
        fn=utils.randomize_seed_fn,
        inputs=[seed, randomize_seed],
        outputs=seed,
        queue=False,
        api_name=False,
    ).then(
        fn=generate,
        inputs=inputs,
        outputs=[result, gr_metadata, history_display],
        api_name="run",
    )
    negative_prompt.submit(
        fn=utils.randomize_seed_fn,
        inputs=[seed, randomize_seed],
        outputs=seed,
        queue=False,
        api_name=False,
    ).then(
        fn=generate,
        inputs=inputs,
        outputs=[result, gr_metadata, history_display],
        api_name=False,
    )
    run_button.click(
        fn=utils.randomize_seed_fn,
        inputs=[seed, randomize_seed],
        outputs=seed,
        queue=False,
        api_name=False,
    ).then(
        fn=generate,
        inputs=inputs,
        outputs=[result, gr_metadata, history_display],
        api_name=False,
    )

demo.queue(max_size=20).launch(debug=IS_COLAB, share=IS_COLAB)