app.py

#!/usr/bin/env python

from __future__ import annotations

import os
import random

import gradio as gr
import numpy as np
import PIL.Image
import spaces
import torch
from diffusers import AutoencoderKL, StableDiffusionXLPipeline
from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
)

DESCRIPTION = "# humblemikey/PixelWave10"
if not torch.cuda.is_available():
    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1024"))
USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1"
ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1"

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
    #vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
    pipe = StableDiffusionXLPipeline.from_pretrained(
        "humblemikey/PixelWave10",
        #vae=vae,
        torch_dtype=torch.float16,
        use_safetensors=True,
        #variant="fp16",
    )

    if ENABLE_CPU_OFFLOAD:
        pipe.enable_model_cpu_offload()
    else:
        pipe.to(device)

    if USE_TORCH_COMPILE:
        pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)


def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    return seed

def get_scheduler(scheduler_config: Dict, name: str) -> Optional[Callable]:
    scheduler_factory_map = {
        "DPM++ 2M Karras": lambda: DPMSolverMultistepScheduler.from_config(scheduler_config, use_karras_sigmas=True),
        "DPM++ SDE Karras": lambda: DPMSolverSinglestepScheduler.from_config(scheduler_config, use_karras_sigmas=True),
        "DPM++ 2M SDE Karras": lambda: DPMSolverMultistepScheduler.from_config(scheduler_config, use_karras_sigmas=True, algorithm_type="sde-dpmsolver++"),
        "Euler": lambda: EulerDiscreteScheduler.from_config(scheduler_config),
        "Euler a": lambda: EulerAncestralDiscreteScheduler.from_config(scheduler_config),
        "DDIM": lambda: DDIMScheduler.from_config(scheduler_config),
    }
    return scheduler_factory_map.get(name, lambda: None)()

@spaces.GPU
def generate(
    prompt: str,
    negative_prompt: str = "",
    use_negative_prompt: bool = False,
    seed: int = 0,
    width: int = 1024,
    height: int = 1024,
    guidance_scale_base: float = 4.0,
    num_inference_steps_base: int = 40,
    sampler: str = "DPM++ 2M SDE Karras",
    progress=gr.Progress(track_tqdm=True)
) -> PIL.Image.Image:
    generator = torch.Generator().manual_seed(seed)

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

    if not use_negative_prompt:
        negative_prompt = None  # type: ignore

    return pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        width=width,
        height=height,
        guidance_scale=guidance_scale_base,
        num_inference_steps=num_inference_steps_base,
        generator=generator,
        output_type="pil",
    ).images[0]
    return image

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

with gr.Blocks(css="style.css") as demo:
    gr.Markdown(DESCRIPTION)
    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=1,
                placeholder="Enter your prompt",
                container=False,
            )
            run_button = gr.Button("Run", scale=0)
        result = gr.Image(label="Result", show_label=False)
    with gr.Accordion("Advanced options", open=False):
        with gr.Row():
            use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False)
        negative_prompt = gr.Text(
            label="Negative prompt",
            max_lines=1,
            placeholder="Enter a negative prompt",
            visible=False,
        )
        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_base = gr.Slider(
                label="Guidance scale for base",
                minimum=1,
                maximum=20,
                step=0.1,
                value=4.0,
            )
            num_inference_steps_base = gr.Slider(
                label="Number of inference steps for base",
                minimum=10,
                maximum=100,
                step=1,
                value=40,
            )

    gr.Examples(
        examples=examples,
        inputs=prompt,
        outputs=result,
        fn=generate,
    )

    use_negative_prompt.change(
        fn=lambda x: gr.update(visible=x),
        inputs=use_negative_prompt,
        outputs=negative_prompt,
        queue=False,
        api_name=False,
    )

    gr.on(
        triggers=[
            prompt.submit,
            negative_prompt.submit,
            run_button.click,
        ],
        fn=randomize_seed_fn,
        inputs=[seed, randomize_seed],
        outputs=seed,
        queue=False,
        api_name=False,
    ).then(
        fn=generate,
        inputs=[
            prompt,
            negative_prompt,
            use_negative_prompt,
            seed,
            width,
            height,
            guidance_scale_base,
            num_inference_steps_base,
        ],
        outputs=result,
        api_name="run",
    )

if __name__ == "__main__":
    demo.queue(max_size=20).launch()