app.py

import gradio as gr
import numpy as np
import random

# import spaces #[uncomment to use ZeroGPU]
from diffusers import DiffusionPipeline, AutoPipelineForText2Image
from peft import PeftModel
import torch

device = "cuda" if torch.cuda.is_available() \
        else "xpu" if torch.xpu.is_available() \
        else "cpu"
current_model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
current_lora_repo = None
current_lora_scale = 1.0

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

pipe = DiffusionPipeline.from_pretrained(current_model_repo_id, torch_dtype=torch_dtype)
pipe = pipe.to(device)

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

def clean_vram():
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
    if torch.xpu.is_available():
        torch.xpu.empty_cache()

# @spaces.GPU #[uncomment to use ZeroGPU]
def infer(
    prompt,
    model_repo,
    lora_repo,
    lora_scale,
    negative_prompt,
    seed,
    randomize_seed,
    width,
    height,
    guidance_scale,
    pag_scale,
    num_inference_steps,
    progress=gr.Progress(track_tqdm=True),
):
    global current_model_repo_id, current_lora_repo, current_lora_scale, pipe

    if lora_repo == "None":
        lora_repo = None

    if (model_repo != current_model_repo_id) or (lora_repo != current_lora_repo) or (current_lora_scale != lora_scale):
        print(f"The model changed to {model_repo}, {lora_repo} lora, reloading pipeline...")
        current_model_repo_id = model_repo
        current_lora_repo = lora_repo
        current_lora_scale = lora_scale
        del pipe
        clean_vram()

        pipe = DiffusionPipeline.from_pretrained(model_repo, torch_dtype=torch_dtype).to(device)
        if lora_repo:
            pipe.unet = PeftModel.from_pretrained(pipe.unet, lora_repo, subfolder="unet").to(device)
            pipe.text_encoder = PeftModel.from_pretrained(pipe.text_encoder, lora_repo, subfolder="text_encoder").to(device)
            pipe.unet.load_state_dict({k: lora_scale*v if 'lora' in k else v for k, v in pipe.unet.state_dict().items()})
            pipe.text_encoder.load_state_dict({k: lora_scale*v if 'lora' in k else v for k, v in pipe.text_encoder.state_dict().items()})

        pipe = AutoPipelineForText2Image.from_pipe(pipe, enable_pag=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,
        pag_scale=pag_scale,
        num_inference_steps=num_inference_steps,
        width=width,
        height=height,
        generator=generator,
    ).images[0]

    clean_vram()

    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:
    with gr.Column(elem_id="col-container"):
        gr.Markdown(" # Text-to-Image Gradio Template")

        model_repo = gr.Dropdown(
            label="Model repository path",
            choices=["stabilityai/sdxl-turbo", "CompVis/stable-diffusion-v1-4", "stable-diffusion-v1-5/stable-diffusion-v1-5"],
            allow_custom_value=True
        )

        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=True,
            )

            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,  # Replace with defaults that work for your model
                )

                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,  # Replace with defaults that work for your model
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=0.0,  # Replace with defaults that work for your model
                )
                
                pag_scale = gr.Slider(
                    label="PAG scale",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=0.0,  # Replace with defaults that work for your model
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=2,  # Replace with defaults that work for your model
                )

            with gr.Row():
                lora_repo = gr.Dropdown(
                    label="LoRA repository path",
                    choices=["None", "AbstractQbit/biskvit_cat_lora"],
                    allow_custom_value=True
                )

                lora_scale = gr.Slider(
                    label="LoRA scale",
                    minimum=0.0,
                    maximum=1.0,
                    step=0.1,
                    value=1.0,  # Replace with defaults that work for your model
                )

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

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