app.py

import torch
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 = "RealVis 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"
ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1"
OUTPUT_DIR = os.getenv("OUTPUT_DIR", "./outputs")

MODEL = os.getenv(
    "MODEL",
    "https://huggingface.co/SG161222/RealVisXL_V4.0/blob/main/RealVisXL_V4.0.safetensors",
)

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

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

def load_pipeline(model_name):
    vae = AutoencoderKL.from_pretrained(
        "madebyollin/sdxl-vae-fp16-fix",
        torch_dtype=torch.float16,
    )
    pipeline = (
        StableDiffusionXLPipeline.from_single_file
        if MODEL.endswith(".safetensors")
        else StableDiffusionXLPipeline.from_pretrained
    )

    pipe = pipeline(
        model_name,
        vae=vae,
        torch_dtype=torch.float16,
        custom_pipeline="lpw_stable_diffusion_xl",
        use_safetensors=True,
        add_watermarker=False,
        use_auth_token=HF_TOKEN,
        variant="fp16",
    )

    pipe.to(device)
    return pipe

@spaces.GPU
def generate(
    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),
) -> list:
    generator = utils.seed_everything(seed)

    width, height = utils.aspect_ratio_handler(
        aspect_ratio_selector,
        custom_width,
        custom_height,
    )

    width, height = utils.preprocess_image_dimensions(width, height)

    backup_scheduler = pipe.scheduler
    pipe.scheduler = utils.get_scheduler(pipe.scheduler.config, sampler)

    metadata = {
        "prompt": prompt,
        "negative_prompt": negative_prompt,
        "resolution": f"{width} x {height}",
        "guidance_scale": guidance_scale,
        "num_inference_steps": num_inference_steps,
        "seed": seed,
        "sampler": sampler,
        "use_upscaler": use_upscaler,
        "upscaler_strength": upscaler_strength,
        "upscale_by": upscale_by,
    }
    logger.info(json.dumps(metadata, indent=4))

    try:
        images = pipe(
            prompt=prompt,
            negative_prompt=negative_prompt,
            width=width,
            height=height,
            guidance_scale=guidance_scale,
            num_inference_steps=num_inference_steps,
            generator=generator,
            output_type="pil",
        ).images

        if use_upscaler:
            images = [image.resize((int(width * upscale_by), int(height * upscale_by))) for image in images]

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

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

def postprocess_images(images):
    return images  # No caption, just return the images

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 ([RealVis XL]https://huggingface.co/SG161222/RealVisXL_V4.0/)""",
        elem_id="subtitle",
    )
    gr.DuplicateButton(
        placeholder="Enter a negative prompt",
        value=""
    )
    aspect_ratio_selector = gr.Radio(
        label="Aspect Ratio",
        choices=config.aspect_ratios,
        value="1024 x 1024",
        container=True,
    )
    with gr.Group(visible=False) as custom_resolution:
        with gr.Row():
            custom_width = gr.Slider(
                label="Width",
                minimum=MIN_IMAGE_SIZE,
                maximum=MAX_IMAGE_SIZE,
                step=8,
                value=1024,
            )
            custom_height = gr.Slider(
                label="Height",
                minimum=MIN_IMAGE_SIZE,
                maximum=MAX_IMAGE_SIZE,
                step=8,
                value=1024,
            )
    use_upscaler = gr.Checkbox(label="Use Upscaler", value=False)
    with gr.Row() as upscaler_row:
        upscaler_strength = gr.Slider(
            label="Strength",
            minimum=0,
            maximum=1,
            step=0.05,
            value=0.55,
            visible=False,
        )
        upscale_by = gr.Slider(
            label="Upscale by",
            minimum=1,
            maximum=1.5,
            step=0.1,
            value=1.5,
            visible=False,
        )

    sampler = gr.Dropdown(
        label="Sampler",
        choices=config.sampler_list,
        interactive=True,
        value="DPM++ 2M SDE Karras",
    )
    with gr.Row():
        seed = gr.Slider(
            label="Seed", minimum=0, maximum=utils.MAX_SEED, step=1, value=0
        )
        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
    with gr.Group():
        with gr.Row():
            guidance_scale = gr.Slider(
                label="Guidance scale",
                minimum=1,
                maximum=12,
                step=0.1,
                value=7.0,
            )
            num_inference_steps = gr.Slider(
                label="Number of inference steps",
                minimum=1,
                maximum=100,
                step=1,
                value=28,
            )
    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],
        fn=lambda *args, **kwargs: generate(*args, use_upscaler=True, **kwargs),
        cache_examples=CACHE_EXAMPLES,
    )
    use_upscaler.change(
        fn=lambda x: [gr.update(visible=x), gr.update(visible=x)],
        inputs=use_upscaler,
        outputs=[upscaler_strength, upscale_by],
        queue=False,
        api_name=False,
    )
    aspect_ratio_selector.change(
        fn=lambda x: gr.update(visible=x == "Custom"),
        inputs=aspect_ratio_selector,
        outputs=custom_resolution,
        queue=False,
        api_name=False,
    )

    inputs = [
        prompt,
        negative_prompt,
        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],
        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],
        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],
        api_name=False,
    )

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