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

device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16

pipe = DiffusionPipeline.from_pretrained("Chan-Y/Cyber-Stable-Realistic", 
                                         torch_dtype=torch.float16).to(device)

MAX_SEED = 999999999999999
MAX_IMAGE_SIZE = 1344

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)
    
    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, seed


examples = [
    ["Batman, cute modern Disney style, Pixar 3d portrait, ultra detailed, gorgeous, 3d zbrush, trending on dribbble, 8k render.",
     "",       
     12345,
     50]
]

css = """
#col-container {
    margin: 0 auto;
    max-width: 580px;
}
"""

with gr.Blocks(css=css) as demo:
    
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""
        # Demo [Chan-Y/Stable-Flash-Lightning](https://huggingface.co/Chan-Y/Chan-Y-Cyber-Stable-Realistic)
        by Cihan Yalçın | My [LinkedIn](https://www.linkedin.com/in/chanyalcin/) My [GitHub](https://github.com/g-hano)
        """)
        
        with gr.Row():
            prompt = gr.Textbox(
                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 Settings", open=False):
            negative_prompt = gr.Textbox(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
            )
            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=64,
                    value=1024,
                )
                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=64,
                    value=1024,
                )
            
            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=5.0,
                )
                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=28,
                )
        
    with gr.Accordion("Examples", open=False):
        gr.Examples(
            examples=examples,
            inputs=[prompt, negative_prompt, seed, num_inference_steps]
        )
    run_button.click(
        fn=infer,
        inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
        outputs=[result, seed]
    )
    
    demo.launch()