app.py

import spaces
import gradio as gr
import numpy as np
import torch
import random
import logging
import utils
from diffusers.models import AutoencoderKL

MAX_SEED = np.iinfo(np.int32).max
MIN_IMAGE_SIZE = 512
MAX_IMAGE_SIZE = 2048

# Enhanced logging configuration
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    datefmt='%Y-%m-%d %H:%M:%S'
)
logger = logging.getLogger(__name__)

# PyTorch settings for better performance and determinism
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
torch.backends.cuda.matmul.allow_tf32 = True

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
logger.info(f"Using device: {device}")

# Model initialization
# if torch.cuda.is_available():
#     try:
#         logger.info("Loading VAE and pipeline...")
#         vae = AutoencoderKL.from_pretrained(
#             "madebyollin/sdxl-vae-fp16-fix",
#             torch_dtype=torch.float16,
#         )
#         pipe = utils.load_pipeline("cagliostrolab/animagine-xl-4.0", device, vae=vae)
#         logger.info("Pipeline loaded successfully on GPU!")
#     except Exception as e:
#         logger.error(f"Error loading VAE, falling back to default: {e}")
#         pipe = utils.load_pipeline("cagliostrolab/animagine-xl-4.0", device)
# else:
#     logger.warning("CUDA not available, running on CPU")
#     pipe = None



@spaces.GPU
def generate(
    prompt: str,
    negative_prompt: str,
    width: int,
    height: int,
    scheduler: str,
    upscaler_strength:float,
    upscale_by:float,
    seed: int,
    randomize_seed: bool,
    guidance_scale: float,
    num_inference_steps: int,
    progress:gr.Progress=gr.Progress(track_tqdm=True),
):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

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

    # 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 image, seed

    return None, seed



scheduler_list = [
    "DPM++ 2M Karras",
    "DPM++ SDE Karras",
    "DPM++ 2M SDE Karras",
    "Euler",
    "Euler a",
    "DDIM"
]


title = "# Animagine XL 4.0 Demo"

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

custom_css = """
#row-container {
    align-items: stretch;
}
#output-image{
    flex-grow: 1;
}
"""


with gr.Blocks(css=custom_css).queue() as demo:
    gr.Markdown(title)
    with gr.Row(
        elem_id="row-container"
    ):
        with gr.Column():
            gr.Markdown("### Input")
            with gr.Column():
                prompt = gr.Text(
                    label="Prompt",
                    max_lines=1,
                    placeholder="Enter your prompt",
                )
                negative_prompt = gr.Text(
                    label="Negative prompt",
                    max_lines=1,
                    placeholder="Enter a negative prompt",
                )
            with gr.Row():
                width = gr.Slider(
                    label="Width",
                    minimum=MIN_IMAGE_SIZE,
                    maximum=MAX_IMAGE_SIZE,
                    step=8,
                    value=832,  
                )
                height = gr.Slider(
                    label="Height",
                    minimum=MIN_IMAGE_SIZE,
                    maximum=MAX_IMAGE_SIZE,
                    step=8,
                    value=1216, 
                )
            with gr.Row():
                upscaler_strength = gr.Slider(
                    label="Upscaler strength",
                    minimum=0,
                    maximum=1,
                    step=0.05,
                    value=0.55,  
                )
                upscale_by = gr.Slider(
                    label="Upscale",
                    minimum=1,
                    maximum=1.5,
                    step=0.1,
                    value=1.5, 
                )
            with gr.Column():
                scheduler = gr.Dropdown(
                            label="scheduler",
                            choices=scheduler_list,
                            interactive=True,
                            value="Euler a",
                        )
            with gr.Column():
                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():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=1.0,
                    maximum=12.0,
                    step=0.1,
                    value=6.0, 
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=25, 
                )
            run_button = gr.Button("Run", variant="primary")

        with gr.Column():
            gr.Markdown("### Output")
            result = gr.Image(
                label="Generated Image",
                elem_id="output-image"
            )
    run_button.click(
        fn=generate,
        inputs=[
            prompt, negative_prompt,
            width, height, 
            scheduler,
            upscaler_strength,upscale_by,
            seed,randomize_seed,
            guidance_scale,num_inference_steps
            ], 
        outputs=[result, seed],
    )  

demo.launch()