import gradio as gr
import numpy as np
import random
from PIL import Image
from rembg import remove

# import spaces #[uncomment to use ZeroGPU]
from peft import PeftModel
from diffusers import DiffusionPipeline, StableDiffusionPipeline, ControlNetModel, StableDiffusionControlNetPipeline, AutoencoderTiny, DDIMScheduler
from diffusers.utils import load_image
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "CompVis/stable-diffusion-v1-4"  # Replace to the model you would like to use

torch_dtype = torch.float16

pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
pipe = pipe.to(device)
# pipe.unet = PeftModel.from_pretrained(pipe.unet, "alexanz/SD14_lora_pusheen")
pipe.safety_checker = None
pipe.requires_safety_checker = False

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 512


# @spaces.GPU #[uncomment to use ZeroGPU]
def load_model(model_id, lora_strength, use_controlnet=False, control_mode="edge_detection", use_ip_adapter=False, control_strength_ip=0.0,
               acceleration_mode=None):
    global pipe
    if pipe is not None:
        del pipe
        torch.cuda.empty_cache()
    try:
        if control_mode == "edge_detection" and (model_id == "CompVis/stable-diffusion-v1-4" or model_id == "alexanz/SD14_lora_pusheen"):
            controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch_dtype)
        elif control_mode == "pose_estimation"and (model_id == "CompVis/stable-diffusion-v1-4" or model_id == "alexanz/SD14_lora_pusheen"):
            controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose", torch_dtype=torch_dtype)
        if control_mode == "edge_detection" and (model_id == "alexanz/SD15_lora_pusheen"):
            controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny", torch_dtype=torch_dtype)
        elif control_mode == "pose_estimation"and (model_id == "alexanz/SD15_lora_pusheen"):
            controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_openpose", torch_dtype=torch_dtype)
    
        if model_id == "CompVis/stable-diffusion-v1-4":
            if use_controlnet:
                pipe = StableDiffusionControlNetPipeline.from_pretrained(
                    model_id,
                    safety_checker=None,
                    controlnet=controlnet,
                    torch_dtype=torch_dtype
                )
            else:
                pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype)

        elif model_id == "alexanz/SD14_lora_pusheen":
            if use_controlnet:
                pipe = StableDiffusionControlNetPipeline.from_pretrained(
                    "CompVis/stable-diffusion-v1-4",
                    safety_checker=None,
                    controlnet=controlnet,
                    torch_dtype=torch_dtype
                )
                pipe.unet = PeftModel.from_pretrained(pipe.unet, model_id, torch_dtype=torch_dtype)
            else:
                pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch_dtype)
                pipe.unet = PeftModel.from_pretrained(pipe.unet, model_id)

        elif model_id == "alexanz/SD15_lora_pusheen":
            if use_controlnet:
                pipe = StableDiffusionControlNetPipeline.from_pretrained(
                    "stable-diffusion-v1-5/stable-diffusion-v1-5",
                    safety_checker=None,
                    controlnet=controlnet,
                    torch_dtype=torch_dtype
                )
                pipe.unet = PeftModel.from_pretrained(pipe.unet, model_id, torch_dtype=torch_dtype)
            else:
                if acceleration_mode is None:
                    pipe = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch_dtype)
                    pipe.unet = PeftModel.from_pretrained(pipe.unet, model_id)
                elif acceleration_mode == "distilled":
                    pipe = StableDiffusionPipeline.from_pretrained(
                        "nota-ai/bk-sdm-small", torch_dtype=torch.float16, use_safetensors=True,
                    )
                elif acceleration_mode == "distilled + tiny":
                    pipe = StableDiffusionPipeline.from_pretrained(
                        "nota-ai/bk-sdm-small", torch_dtype=torch.float16, use_safetensors=True,
                    )
                    pipe.vae = AutoencoderTiny.from_pretrained(
                        "sayakpaul/taesd-diffusers", torch_dtype=torch.float16, use_safetensors=True,
                    )
                elif acceleration_mode == "DDIM":
                    scheduler = DDIMScheduler.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="scheduler")
                    pipe = StableDiffusionPipeline.from_pretrained(
                        "stable-diffusion-v1-5/stable-diffusion-v1-5", scheduler=scheduler, torch_dtype=torch.float16
                    )

        if use_ip_adapter:
            pipe.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
            pipe.set_ip_adapter_scale(control_strength_ip)

        pipe = pipe.to(device)
        pipe.safety_checker = None
        pipe.requires_safety_checker = False
        pipe.enable_model_cpu_offload()
        return f"Model {model_id} loaded with ControlNet: {use_controlnet}, mode: {control_mode}"
    except Exception as e:
        return f"Error: {str(e)}"


def infer(
        prompt,
        negative_prompt,
        seed,
        randomize_seed,
        width,
        height,
        lora_strength,
        guidance_scale,
        num_inference_steps,
        use_controlnet,
        control_image_cont,
        control_strength_cont,
        model_dropdown,
        control_mode,
        use_ip_adapter,
        control_strength_ip,
        control_image_ip,
        use_rmbg,
        acceleration_mode,
        progress=gr.Progress(track_tqdm=True),
):
    load_status = load_model(
        model_dropdown,
        lora_strength,
        use_controlnet,
        control_mode,
        use_ip_adapter,
        control_strength_ip,
        acceleration_mode
    )
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    generator = torch.Generator().manual_seed(seed)

    if use_controlnet and control_image_cont is None:
        return None, seed, "⚠️ ControlNet need control_image!"
    
    if use_ip_adapter and control_image_ip is None:
        return None, seed, "⚠️ IP-adapter need control_image!"

    if use_controlnet:
        control_image_cont= Image.fromarray(control_image_cont)
        control_strength_cont = float(control_strength_cont)
    if use_ip_adapter:
        control_image_ip = Image.fromarray(control_image_ip)

    image = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        width=width,
        height=height,
        generator=generator,
        image=control_image_cont if use_controlnet else None,
        controlnet_conditioning_scale=control_strength_cont if use_controlnet else None,
        ip_adapter_image=control_image_ip if use_ip_adapter else None,
        cross_attention_kwargs={"scale": lora_strength}
    ).images[0]

    if use_rmbg:
        image = remove(image)

    return image, seed, "Model ready"


examples = [
    "Sticker of Pusheen. Gray cat holding a heart-shaped balloon, standing next to a Valentine’s card with 'You’re Pawesome' written in glitter.",
    "Gray cat holding a heart-shaped balloon, standing next to a Valentine’s card with 'You’re Pawesome' written in glitter.",
    "Sticker of Pusheen. Pusheen riding a shopping cart full of cupcakes.",
    "Sticker of Pusheen. A cat with droopy ears and a patched scarf, sitting on a park bench at dusk, holding a photo of another cat, with autumn leaves falling around it.",
    "Sticker of Pusheen. A cartoon grey cat asks for a fish in a word cloud.",
    "Sticker of Pusheen. Pusheen tangled in yarn, playful annoyed face."
]

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_dropdown = gr.Dropdown(label="Model ID",
                                     choices=["alexanz/SD14_lora_pusheen", "CompVis/stable-diffusion-v1-4", "alexanz/SD15_lora_pusheen"],
                                     value="CompVis/stable-diffusion-v1-4")
        model_status = gr.Textbox(label="Model Status", interactive=False)

        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",
            )

            lora_strength = gr.Slider(
                label="Lora strength",
                minimum=0.0,
                maximum=1.0,
                step=0.1,
                value=1.0,
            )

            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,  # Replace with defaults that work for your model
                )

                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,  # 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=7.5,  # 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=20,  # Replace with defaults that work for your model
                )

        use_controlnet = gr.Checkbox(label="Use ControlNet", value=False)
        with gr.Accordion("ControlNet Settings", open=True, visible=False) as controlnet_settings:
            control_mode = gr.Dropdown(
                label="ControlNet Mode",
                choices=["edge_detection", "pose_estimation"],
                value="edge_detection"
            )
            control_strength_cont = gr.Slider(
                label="Control Strength",
                minimum=0.0,
                maximum=2.0,
                step=0.1,
                value=1.0
            )
            control_image_cont = gr.Image(label="Control Image", type="numpy")

        use_ip_adapter = gr.Checkbox(label="Use IP-adapter", value=False)
        with gr.Accordion("IP-adapter Settings", open=True, visible=False) as ip_adapter_settings:
            control_strength_ip = gr.Slider(
                label="Control Strength",
                minimum=0.0,
                maximum=2.0,
                step=0.1,
                value=1.0
            )
            control_image_ip = gr.Image(label="Control Image (IP-adapter)", type="numpy")

        use_rmbg = gr.Checkbox(label="Delete background?", value=False)

        use_acceleration = gr.Checkbox(label="Use accelerate model? (only for 1.5 SD!)", value=False)
        with gr.Accordion("Acceleration Settings", open=True, visible=False) as acceleration_settings:
            acceleration_mode = gr.Dropdown(label="Acceleration mode",
                                            choices=["distilled", "distilled + tiny", "DDIM"],
                                            value=None)

        gr.Examples(examples=examples, inputs=[prompt])

    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            prompt,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            lora_strength,
            guidance_scale,
            num_inference_steps,
            use_controlnet,
            control_image_cont,
            control_strength_cont,
            model_dropdown,
            control_mode,
            use_ip_adapter,
            control_strength_ip,
            control_image_ip,
            use_rmbg,
            acceleration_mode
        ],
        outputs=[result, seed, model_status],
    )

    use_controlnet.change(
        fn=lambda x: gr.update(visible=x, value=None),
        inputs=[use_controlnet],
        outputs=[controlnet_settings]
    )

    use_ip_adapter.change(
        fn=lambda x: gr.update(visible=x, value=None),
        inputs=[use_ip_adapter],
        outputs=[ip_adapter_settings]
    )

    use_rmbg.change(
        fn=lambda x: gr.update(visible=x, value=None),
        inputs=[use_rmbg]
    )

    use_acceleration.change(
        fn=lambda x: gr.update(visible=x, value=None),
        inputs=[use_acceleration],
        outputs=[acceleration_settings]
    )

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