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")

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")

# Load pipeline function remains unchanged

def parse_json_parameters(json_str):
    try:
        params = json.loads(json_str)
        return params
    except json.JSONDecodeError:
        return None

def apply_json_parameters(json_str):
    params = parse_json_parameters(json_str)
    if params:
        return (
            params.get("prompt", ""),
            params.get("negative_prompt", ""),
            params.get("seed", 0),
            params.get("width", 1024),
            params.get("height", 1024),
            params.get("guidance_scale", 7.0),
            params.get("num_inference_steps", 30),
            params.get("sampler", "DPM++ 2M SDE Karras"),
            params.get("aspect_ratio", "1024 x 1024"),
            params.get("use_upscaler", False),
            params.get("upscaler_strength", 0.55),
            params.get("upscale_by", 1.5),
        )
    return [gr.update()] * 12

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:
    # Existing generate function code...

    # Update history after generation
    history = gr.get_state("history") or []
    history.insert(0, {"prompt": prompt, "image": images[0], "metadata": metadata})
    gr.set_state("history", history[:10])  # Keep only the last 10 entries

    return images, metadata, gr.update(choices=[h["prompt"] for h in history])

def get_random_prompt():
    return random.choice(config.examples)

with gr.Blocks(css="style.css") as demo:
    # Existing UI elements...

    with gr.Accordion(label="JSON Parameters", open=False):
        json_input = gr.TextArea(label="Input JSON parameters")
        apply_json_button = gr.Button("Apply JSON Parameters")

    with gr.Row():
        clear_button = gr.Button("Clear All")
        random_prompt_button = gr.Button("Random Prompt")

    history_dropdown = gr.Dropdown(label="Generation History", choices=[], interactive=True)

    # Connect components
    apply_json_button.click(
        fn=apply_json_parameters,
        inputs=json_input,
        outputs=[prompt, negative_prompt, seed, custom_width, custom_height,
                 guidance_scale, num_inference_steps, sampler,
                 aspect_ratio_selector, use_upscaler, upscaler_strength, upscale_by]
    )

    clear_button.click(
        fn=lambda: (gr.update(value=""), gr.update(value=""), gr.update(value=0),
                    gr.update(value=1024), gr.update(value=1024),
                    gr.update(value=7.0), gr.update(value=30),
                    gr.update(value="DPM++ 2M SDE Karras"),
                    gr.update(value="1024 x 1024"), gr.update(value=False),
                    gr.update(value=0.55), gr.update(value=1.5)),
        inputs=[],
        outputs=[prompt, negative_prompt, seed, custom_width, custom_height,
                 guidance_scale, num_inference_steps, sampler,
                 aspect_ratio_selector, use_upscaler, upscaler_strength, upscale_by]
    )

    random_prompt_button.click(
        fn=get_random_prompt,
        inputs=[],
        outputs=prompt
    )

    history_dropdown.change(
        fn=lambda x: gr.update(value=x),
        inputs=history_dropdown,
        outputs=prompt
    )

    # Existing event handlers...

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