import os
import spaces
import torch
from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
import gradio as gr
import random
import tqdm

# Enable TQDM progress tracking
tqdm.monitor_interval = 0

# Load the diffusion pipelines
pipe1 = StableDiffusionXLPipeline.from_pretrained(
    "kayfahaarukku/UrangDiffusion-1.4", 
    torch_dtype=torch.float16, 
    custom_pipeline="lpw_stable_diffusion_xl",
)
pipe1.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe1.scheduler.config)

pipe2 = StableDiffusionXLPipeline.from_pretrained(
    "kayfahaarukku/UrangDiffusion-2.0", 
    torch_dtype=torch.float16, 
    custom_pipeline="lpw_stable_diffusion_xl",
)
pipe2.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe2.scheduler.config)

# Function to generate images from both models
@spaces.GPU
def generate_comparison(prompt, negative_prompt, use_defaults, resolution, guidance_scale, num_inference_steps, seed, randomize_seed, progress=gr.Progress()):
    pipe1.to('cuda')
    pipe2.to('cuda')
    
    if randomize_seed:
        seed = random.randint(0, 99999999)
    if use_defaults:
        prompt = f"{prompt}, best quality, amazing quality, very aesthetic"
        negative_prompt = f"nsfw, lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract], {negative_prompt}"
    generator = torch.manual_seed(seed)
    
    def callback(step, timestep, latents):
        progress(step / (2 * num_inference_steps))
        return
    
    width, height = map(int, resolution.split('x'))
    
    # Generate image with UrangDiffusion-1.4
    image1 = pipe1(
        prompt, 
        negative_prompt=negative_prompt,
        width=width,
        height=height, 
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        generator=generator,
        callback=callback,
        callback_steps=1
    ).images[0]

    # Generate image with UrangDiffusion-2.0
    image2 = pipe2(
        prompt, 
        negative_prompt=negative_prompt,
        width=width,
        height=height, 
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        generator=generator,
        callback=callback,
        callback_steps=1
    ).images[0]

    torch.cuda.empty_cache()

    metadata_text = f"{prompt}\nNegative prompt: {negative_prompt}\nSteps: {num_inference_steps}, Sampler: Euler a, Size: {width}x{height}, Seed: {seed}, CFG scale: {guidance_scale}"

    return image1, image2, seed, metadata_text

# Define Gradio interface
def interface_fn(prompt, negative_prompt, use_defaults, resolution, guidance_scale, num_inference_steps, seed, randomize_seed, progress=gr.Progress()):
    image1, image2, seed, metadata_text = generate_comparison(prompt, negative_prompt, use_defaults, resolution, guidance_scale, num_inference_steps, seed, randomize_seed, progress)
    return image1, image2, seed, gr.update(value=metadata_text)

def reset_inputs():
    return gr.update(value=''), gr.update(value=''), gr.update(value=True), gr.update(value='832x1216'), gr.update(value=7), gr.update(value=28), gr.update(value=0), gr.update(value=True), gr.update(value='')

with gr.Blocks(title="UrangDiffusion Comparison Demo", theme="NoCrypt/miku@1.2.1") as demo:
    gr.HTML(
        "<h1>UrangDiffusion 1.4 vs 2.0 Comparison Demo</h1>"
        "This demo showcases a comparison between UrangDiffusion 1.4 and 2.0."
        )
    with gr.Row():
        with gr.Column():
            prompt_input = gr.Textbox(lines=2, placeholder="Enter prompt here", label="Prompt")
            negative_prompt_input = gr.Textbox(lines=2, placeholder="Enter negative prompt here", label="Negative Prompt")
            use_defaults_input = gr.Checkbox(label="Use Default Quality Tags and Negative Prompt", value=True)
            resolution_input = gr.Radio(
                choices=[
                    "1024x1024", "1152x896", "896x1152", "1216x832", "832x1216",
                    "1344x768", "768x1344", "1536x640", "640x1536"
                ],
                label="Resolution",
                value="832x1216"
            )
            guidance_scale_input = gr.Slider(minimum=1, maximum=20, step=0.5, label="Guidance Scale", value=7)
            num_inference_steps_input = gr.Slider(minimum=1, maximum=100, step=1, label="Number of Inference Steps", value=28)
            seed_input = gr.Slider(minimum=0, maximum=999999999, step=1, label="Seed", value=0, interactive=True)
            randomize_seed_input = gr.Checkbox(label="Randomize Seed", value=True)
            generate_button = gr.Button("Generate Comparison")
            reset_button = gr.Button("Reset")

        with gr.Column():
            with gr.Row():
                output_image1 = gr.Image(type="pil", label="UrangDiffusion 1.4")
                output_image2 = gr.Image(type="pil", label="UrangDiffusion 2.0")
            with gr.Accordion("Parameters", open=False):
                gr.Markdown(
                    """
                    This parameter is compatible with Stable Diffusion WebUI's parameter importer.
                    """
                )
                metadata_textbox = gr.Textbox(lines=6, label="Image Parameters", interactive=False, max_lines=6)
            gr.Markdown(
                """
                ### Recommended prompt formatting:
                `1girl/1boy, character name, from what series, everything else in any order, best quality, amazing quality, very aesthetic`

                **PS:** `best quality, amazing quality, very aesthetic` is automatically added when "Use Default Quality Tags and Negative Prompt" is enabled

                ### Recommended settings:
                - Steps: 25-30
                - CFG: 5-7
                """
            )

    generate_button.click(
        interface_fn,
        inputs=[
            prompt_input, negative_prompt_input, use_defaults_input, resolution_input, guidance_scale_input, num_inference_steps_input, seed_input, randomize_seed_input
        ],
        outputs=[output_image1, output_image2, seed_input, metadata_textbox]
    )
    
    reset_button.click(
        reset_inputs,
        inputs=[],
        outputs=[
            prompt_input, negative_prompt_input, use_defaults_input, resolution_input, guidance_scale_input, num_inference_steps_input, seed_input, randomize_seed_input, metadata_textbox
        ]
    )

demo.queue(max_size=20).launch(share=False)