app.py

import gradio as gr
import numpy as np
import random
import os
from pathlib import Path

# import spaces #[uncomment to use ZeroGPU]
from diffusers import DiffusionPipeline, StableDiffusionPipeline, schedulers
import torch

MODEL_REPO_ID = os.environ.get('MODEL_REPO_ID', 'myxlmynx/cyberrealistic_classic40')
MODEL_REPO_LOCAL = os.environ.get('MODEL_REPO_LOCAL', '')
MODEL_REPO_NAME = os.environ.get('MODEL_REPO_NAME', 'CyberRealistic Classic 4.0')

device = "cuda" if torch.cuda.is_available() else "cpu"
print("Running on " + device)

if torch.cuda.is_available():
    torch_dtype = torch.float16
else:
    torch_dtype = torch.float32

print("Loading " + MODEL_REPO_ID)
if MODEL_REPO_LOCAL and Path(MODEL_REPO_LOCAL).is_file():
    pipe = StableDiffusionPipeline.from_single_file(MODEL_REPO_LOCAL, torch_dtype=torch_dtype)
else:
    pipe = DiffusionPipeline.from_pretrained(MODEL_REPO_ID, torch_dtype=torch_dtype)

extra_inference_parameters = {}

# add accel LoRA to boost generation speed
pipe.load_lora_weights("wangfuyun/PCM_Weights",
    subfolder='sd15', weight_name='pcm_sd15_smallcfg_2step_converted.safetensors',
    adapter_name='pcm_smallcfg_2step')
pipe.set_adapters(['pcm_smallcfg_2step'], adapter_weights=[1.0])
pipe.fuse_lora()

# for very low step counts with PCM
#pipe.scheduler = schedulers.DDIMScheduler(timestep_spacing='trailing',
#  clip_sample=False, set_alpha_to_one=False)
pipe.scheduler = schedulers.TCDScheduler()
extra_inference_parameters['eta'] = 0.3
#pipe.scheduler = schedulers.LCMScheduler()
#pipe.scheduler = schedulers.EulerAncestralDiscreteScheduler()

# lib default will fry the image
default_guidance_scale = 1

pipe = pipe.to(device)

MAX_SEED = np.iinfo(np.int32).max
MIN_IMAGE_SIZE = 128
MAX_IMAGE_SIZE = 1024

# @spaces.GPU #[uncomment to use ZeroGPU]
def infer(
    prompt,
    negative_prompt,
    seed,
    randomize_seed,
    width,
    height,
    guidance_scale,
    num_inference_steps,
    progress=gr.Progress(track_tqdm=True),
):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

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

    if guidance_scale == 0:
        guidance_scale = default_guidance_scale

    image = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        width=width,
        height=height,
        generator=generator,
        **extra_inference_parameters
    ).images[0]

    return image, seed


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

css = """
#col-container {
    margin: 0 auto;
    max-width: 640px;
}
"""

with gr.Blocks(css=css) as demo_device:
    with gr.Column(elem_id="col-container"):
        gr.Markdown("# " + MODEL_REPO_NAME + " - on " + device.upper())

        if device == 'cpu':
            gr.Markdown("Note: running on CPU, generation will be very slow. Expect at least" +
              " a minute for minimal parameters (512x512 image, guidance <= 1, <=4 steps).\n" +
              "It's also on a single queue, so clone this space for experimenting with it.")

        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, variant="primary")

        result = gr.Image(label="Result", show_label=False)

        with gr.Accordion("Advanced Settings", open=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=MIN_IMAGE_SIZE,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,
                )

                height = gr.Slider(
                    label="Height",
                    minimum=MIN_IMAGE_SIZE,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=768,
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=0.0,
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=3,
                )

        gr.Examples(examples=examples, inputs=[prompt])
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            prompt,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
        ],
        outputs=[result, seed],
    )


demo_inference = gr.load(MODEL_REPO_ID, title=MODEL_REPO_NAME, src='models')

demo = gr.TabbedInterface([demo_inference, demo_device], ["Inference API", device.upper()])

if __name__ == "__main__":
    demo.launch()