modules/events/flux_events.py

import json
from typing import List

import spaces
import gradio as gr
from huggingface_hub import ModelCard

from modules.helpers.common_helpers import ControlNetReq, BaseReq, BaseImg2ImgReq, BaseInpaintReq
from modules.helpers.flux_helpers import gen_img
from config import flux_loras

loras = flux_loras


# Event functions
def update_fast_generation(fast_generation):
    if fast_generation:
        return (
            gr.update(
                value=3.5
            ),
            gr.update(
                value=8
            )
        )


def selected_lora_from_gallery(evt: gr.SelectData):
    return (
        gr.update(
            value=evt.index
        )
    )


def update_selected_lora(custom_lora):
    link = custom_lora.split("/")
    
    if len(link) == 2:
        model_card = ModelCard.load(custom_lora)
        trigger_word = model_card.data.get("instance_prompt", "")
        image_url = f"""https://huggingface.co/{custom_lora}/resolve/main/{model_card.data.get("widget", [{}])[0].get("output", {}).get("url", None)}"""
        
        custom_lora_info_css = """
        <style>
            .custom-lora-info {
                font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Oxygen', 'Ubuntu', 'Cantarell', 'Fira Sans', 'Droid Sans', 'Helvetica Neue', sans-serif;
                background: linear-gradient(135deg, #4a90e2, #7b61ff);
                color: white;
                padding: 16px;
                border-radius: 8px;
                box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
                margin: 16px 0;
            }
            .custom-lora-header {
                font-size: 18px;
                font-weight: 600;
                margin-bottom: 12px;
            }
            .custom-lora-content {
                display: flex;
                align-items: center;
                background-color: rgba(255, 255, 255, 0.1);
                border-radius: 6px;
                padding: 12px;
            }
            .custom-lora-image {
                width: 80px;
                height: 80px;
                object-fit: cover;
                border-radius: 6px;
                margin-right: 16px;
            }
            .custom-lora-text h3 {
                margin: 0 0 8px 0;
                font-size: 16px;
                font-weight: 600;
            }
            .custom-lora-text small {
                font-size: 14px;
                opacity: 0.9;
            }
            .custom-trigger-word {
                background-color: rgba(255, 255, 255, 0.2);
                padding: 2px 6px;
                border-radius: 4px;
                font-weight: 600;
            }
        </style>
        """

        custom_lora_info_html = f"""
        <div class="custom-lora-info">
            <div class="custom-lora-header">Custom LoRA: {custom_lora}</div>
            <div class="custom-lora-content">
                <img class="custom-lora-image" src="{image_url}" alt="LoRA preview">
                <div class="custom-lora-text">
                    <h3>{link[1].replace("-", " ").replace("_", " ")}</h3>
                    <small>{"Using: <span class='custom-trigger-word'>"+trigger_word+"</span> as the trigger word" if trigger_word else "No trigger word found. If there's a trigger word, include it in your prompt"}</small>
                </div>
            </div>
        </div>
        """

        custom_lora_info_html = f"{custom_lora_info_css}{custom_lora_info_html}"

        return (
            gr.update( # selected_lora
                value=custom_lora,
            ),
            gr.update( # custom_lora_info
                value=custom_lora_info_html,
                visible=True
            )
        )

    else:
        return (
            gr.update( # selected_lora
                value=custom_lora,
            ),
            gr.update( # custom_lora_info
                value=custom_lora_info_html if len(link) == 0 else "",
                visible=False
            )
        )


def add_to_enabled_loras(selected_lora, enabled_loras):
    lora_data = loras
    try:
        selected_lora = int(selected_lora)
        
        if 0 <= selected_lora: # is the index of the lora in the gallery
            lora_info = lora_data[selected_lora]
            enabled_loras.append({
                "repo_id": lora_info["repo"],
                "trigger_word": lora_info["trigger_word"]
            })
    except ValueError:
        link = selected_lora.split("/")
        if len(link) == 2:
            model_card = ModelCard.load(selected_lora)
            trigger_word = model_card.data.get("instance_prompt", "")
            enabled_loras.append({
                "repo_id": selected_lora,
                "trigger_word": trigger_word
            })
    
    return (
        gr.update( # selected_lora
            value=""
        ),
        gr.update( # custom_lora_info
            value="",
            visible=False
        ),
        gr.update( # enabled_loras
            value=enabled_loras
        )
    )


def update_lora_sliders(enabled_loras):
    sliders = []
    remove_buttons = []
    
    for lora in enabled_loras:
        sliders.append(
            gr.update(
                label=lora.get("repo_id", ""),
                info=f"Trigger Word: {lora.get('trigger_word', '')}",
                visible=True,
                interactive=True
            )
        )
        remove_buttons.append(
            gr.update(
                visible=True,
                interactive=True
            )
        )
    
    if len(sliders) < 6:
        for i in range(len(sliders), 6):
            sliders.append(
                gr.update(
                    visible=False
                )
            )
            remove_buttons.append(
                gr.update(
                    visible=False
                )
            )
    
    return *sliders, *remove_buttons


def remove_from_enabled_loras(enabled_loras, index):
    enabled_loras.pop(index)
    return (
        gr.update(
            value=enabled_loras
        )
    )


@spaces.GPU
def generate_image(
        model, prompt, fast_generation, enabled_loras,
        lora_slider_0, lora_slider_1, lora_slider_2, lora_slider_3, lora_slider_4, lora_slider_5,
        img2img_image, inpaint_image, canny_image, pose_image, depth_image,
        img2img_strength, inpaint_strength, canny_strength, pose_strength, depth_strength,
        resize_mode,
        scheduler, image_height, image_width, image_num_images_per_prompt,
        image_num_inference_steps, image_guidance_scale, image_seed,
        refiner, vae
    ):
        base_args = {
            "model": model,
            "prompt": prompt,
            "fast_generation": fast_generation,
            "loras": None,
            "resize_mode": resize_mode,
            "scheduler": scheduler,
            "height": int(image_height),
            "width": int(image_width),
            "num_images_per_prompt": float(image_num_images_per_prompt),
            "num_inference_steps": float(image_num_inference_steps),
            "guidance_scale": float(image_guidance_scale),
            "seed": int(image_seed),
            "refiner": refiner,
            "vae": vae,
            "controlnet_config": None,
        }
        base_args = BaseReq(**base_args)
        
        if len(enabled_loras) > 0:
            base_args.loras = []
            for enabled_lora, slider in zip(enabled_loras, [lora_slider_0, lora_slider_1, lora_slider_2, lora_slider_3, lora_slider_4, lora_slider_5]):
                if enabled_lora['repo_id']:
                    base_args.loras.append({
                        "repo_id": enabled_lora['repo_id'],
                        "weight": slider
                    })
        
        image = None
        mask_image = None
        strength = None
        
        if img2img_image:
            image = img2img_image
            strength = float(img2img_strength)
            
            base_args = BaseImg2ImgReq(
                **base_args.__dict__,
                image=image,
                strength=strength
            )
        elif inpaint_image:
            image = inpaint_image['background'] if not all(pixel == (0, 0, 0) for pixel in list(inpaint_image['background'].getdata())) else None
            mask_image = inpaint_image['layers'][0] if image else None
            strength = float(inpaint_strength)
            
            if image and mask_image:
                base_args = BaseInpaintReq(
                    **base_args.__dict__,
                    image=image,
                    mask_image=mask_image,
                    strength=strength
                )
        elif any([canny_image, pose_image, depth_image]):
            base_args.controlnet_config = ControlNetReq(
                controlnets=[],
                control_images=[],
                controlnet_conditioning_scale=[]
            )
            
            if canny_image:
                base_args.controlnet_config.controlnets.append("canny")
                base_args.controlnet_config.control_images.append(canny_image)
                base_args.controlnet_config.controlnet_conditioning_scale.append(float(canny_strength))
            if pose_image:
                base_args.controlnet_config.controlnets.append("pose")
                base_args.controlnet_config.control_images.append(pose_image)
                base_args.controlnet_config.controlnet_conditioning_scale.append(float(pose_strength))
            if depth_image:
                base_args.controlnet_config.controlnets.append("depth")
                base_args.controlnet_config.control_images.append(depth_image)
                base_args.controlnet_config.controlnet_conditioning_scale.append(float(depth_strength))
        else:
            base_args = BaseReq(**base_args.__dict__)
        
        return gr.update(
            value=gen_img(base_args),
            interactive=True
        )