import gradio as gr
import torch
from diffusers import StableDiffusionXLPipeline, AutoencoderKL
from blora_utils import BLOCKS, filter_lora, scale_lora

vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipeline = StableDiffusionXLPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-base-1.0",
        vae=vae,
        torch_dtype=torch.float16,
    ).to("cuda")

def load_b_lora_to_unet(pipe, content_lora_model_id: str = '', style_lora_model_id: str = '', content_alpha: float = 1.,
                            style_alpha: float = 1.) -> None:
        try:
            # Get Content B-LoRA SD
            if content_lora_model_id:
                content_B_LoRA_sd, _ = pipe.lora_state_dict(content_lora_model_id)
                content_B_LoRA = filter_lora(content_B_LoRA_sd, BLOCKS['content'])
                content_B_LoRA = scale_lora(content_B_LoRA, content_alpha)
            else:
                content_B_LoRA = {}

            # Get Style B-LoRA SD
            if style_lora_model_id:
                style_B_LoRA_sd, _ = pipe.lora_state_dict(style_lora_model_id)
                style_B_LoRA = filter_lora(style_B_LoRA_sd, BLOCKS['style'])
                style_B_LoRA = scale_lora(style_B_LoRA, style_alpha)
            else:
                style_B_LoRA = {}

            # Merge B-LoRAs SD
            res_lora = {**content_B_LoRA, **style_B_LoRA}

            # Load
            pipe.load_lora_into_unet(res_lora, None, pipe.unet)
        except Exception as e:
            raise type(e)(f'failed to load_b_lora_to_unet, due to: {e}')

def main(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
    content_B_LoRA_path = ''
    style_B_LoRA_path = 'fffiloni/b_lora_trained_test_7'
    content_alpha,style_alpha = 1,1.1

    load_b_lora_to_unet(pipeline, content_B_LoRA_path, style_B_LoRA_path, content_alpha, style_alpha)
    prompt = 'An eagle in [v42] style'
    image = pipeline(
        prompt,
        generator=torch.Generator(device="cuda").manual_seed(48), 
        num_images_per_prompt=1
    ).images[0].resize((512,512))
    
    pipeline.unload_lora_weights()
    
    return image

css="""
#col-container {
    margin: 0 auto;
    max-width: 520px;
}
"""

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

with gr.Blocks(css=css) as demo:
    
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""
        # Text-to-Image Gradio Template
        Currently running on {power_device}.
        """)
        
        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 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=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=1,
                    maximum=50,
                    step=1,
                    value=50,
                )
        
        gr.Examples(
            examples = examples,
            inputs = [prompt]
        )

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

demo.queue().launch()