import gradio as gr
import numpy as np
import random
from diffusers import DiffusionPipeline
import torch

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

if torch.cuda.is_available():
    torch.cuda.max_memory_allocated(device=device)
    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
    pipe.enable_xformers_memory_efficient_attention()
    pipe = pipe.to(device)
else: 
    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
    pipe = pipe.to(device)

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

def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):

    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

examples = [
    ["穿着黑色T恤衫，上面中文绿色大字写着“可图”", "image/test_ip.jpg", 0.5],
    ["一只可爱的小狗在奔跑", "image/test_ip2.jpg", 0.5]
]

if torch.cuda.is_available():
    power_device = "GPU"
else:
    power_device = "CPU"

css="""
#col-container {
    margin: 0 auto;
    max-width: 650px;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Row():
        gr.Markdown(f"""
        # Kolors-IP-Adapter-Plus
        Currently running on {power_device}.
        """)
        
    with gr.Row():
        with gr.Column(elem_id="col-container"):
            with gr.Row():
                ip_adapter_image = gr.Image(label="IP-Adapter Image", type="pil")
            with gr.Row():
                ip_adapter_scale = gr.Slider(
                    label="Image influence scale",
                    info="Use 1 for creating variations",
                    minimum=0.0,
                    maximum=1.0,
                    step=0.05,
                    value=0.5,
                )
            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)
            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=512,
                    )
                    height = gr.Slider(
                        label="Height",
                        minimum=256,
                        maximum=MAX_IMAGE_SIZE,
                        step=32,
                        value=512,
                    )
                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=10,
                        maximum=50,
                        step=1,
                        value=25,
                    )
            
        with gr.Column(elem_id="col-container"):
            result = gr.Image(label="Result", show_label=False)
    
    with gr.Row():
        gr.Examples(
                examples = examples,
                inputs = [prompt, ip_adapter_image, ip_adapter_scale]
            )

    run_button.click(
        fn = infer,
        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
        outputs = [result]
    )

demo.queue().launch()