app.py

import gradio as gr

import argparse
import os

import gradio as gr
import huggingface_hub
import numpy as np
import onnxruntime as rt
import pandas as pd
from PIL import Image




# Daftar model dan ControlNet
models = ["Model A", "Model B", "Model C"]
vae = ["VAE A", "VAE B", "VAE C"]
controlnet_types = ["Canny", "Depth", "Normal", "Pose"]
schedulers = ["Euler", "LMS", "DDIM"]


# Fungsi placeholder
def load_model(selected_model):
    return f"Model {selected_model} telah dimuat."

def generate_image(prompt, neg_prompt, width, height, scheduler, num_steps, num_images, cfg_scale, seed, model):
    # Logika untuk menghasilkan gambar dari teks menggunakan model
    return [f"Gambar {i+1} untuk prompt '{prompt}' dengan model '{model}'" for i in range(num_images)], {"prompt": prompt, "neg_prompt": neg_prompt}

def process_image(image, prompt, neg_prompt, model):
    # Logika untuk memproses gambar menggunakan model
    return f"Proses gambar dengan prompt '{prompt}' dan model '{model}'"

def controlnet_process(image, controlnet_type, model):
    # Logika untuk memproses gambar menggunakan ControlNet
    return f"Proses gambar dengan ControlNet '{controlnet_type}' dan model '{model}'"


def controlnet_process_func(image, controlnet_type, model):
    # Update fungsi sesuai kebutuhan
    return controlnet_process(image, controlnet_type, model)

def intpaint_func (image, controlnet_type, model):
    # Update fungsi sesuai kebutuhan
    return controlnet_process(image, controlnet_type, model)
        


#wd tagger

# Dataset v3 series of models:
SWINV2_MODEL_DSV3_REPO = "SmilingWolf/wd-swinv2-tagger-v3"
CONV_MODEL_DSV3_REPO = "SmilingWolf/wd-convnext-tagger-v3"
VIT_MODEL_DSV3_REPO = "SmilingWolf/wd-vit-tagger-v3"
VIT_LARGE_MODEL_DSV3_REPO = "SmilingWolf/wd-vit-large-tagger-v3"
EVA02_LARGE_MODEL_DSV3_REPO = "SmilingWolf/wd-eva02-large-tagger-v3"

# Dataset v2 series of models:
MOAT_MODEL_DSV2_REPO = "SmilingWolf/wd-v1-4-moat-tagger-v2"
SWIN_MODEL_DSV2_REPO = "SmilingWolf/wd-v1-4-swinv2-tagger-v2"
CONV_MODEL_DSV2_REPO = "SmilingWolf/wd-v1-4-convnext-tagger-v2"
CONV2_MODEL_DSV2_REPO = "SmilingWolf/wd-v1-4-convnextv2-tagger-v2"
VIT_MODEL_DSV2_REPO = "SmilingWolf/wd-v1-4-vit-tagger-v2"

# Files to download from the repos
MODEL_FILENAME = "model.onnx"
LABEL_FILENAME = "selected_tags.csv"

# https://github.com/toriato/stable-diffusion-webui-wd14-tagger/blob/a9eacb1eff904552d3012babfa28b57e1d3e295c/tagger/ui.py#L368
kaomojis = [ "0_0", "(o)_(o)", "+_+", "+_-", "._.", "<o>_<o>", "<|>_<|>", "=_=", ">_<", "3_3", "6_9", ">_o", "@_@", "^_^", "o_o", "u_u", "x_x", "|_|", "||_||", ] 

def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser()
    parser.add_argument("--score-slider-step", type=float, default=0.05)
    parser.add_argument("--score-general-threshold", type=float, default=0.35)
    parser.add_argument("--score-character-threshold", type=float, default=0.85)
    parser.add_argument("--share", action="store_true")
    return parser.parse_args()


def load_labels(dataframe) -> list[str]:
    name_series = dataframe["name"]
    name_series = name_series.map(
        lambda x: x.replace("_", " ") if x not in kaomojis else x
    )
    tag_names = name_series.tolist()

    rating_indexes = list(np.where(dataframe["category"] == 9)[0])
    general_indexes = list(np.where(dataframe["category"] == 0)[0])
    character_indexes = list(np.where(dataframe["category"] == 4)[0])
    return tag_names, rating_indexes, general_indexes, character_indexes


def mcut_threshold(probs):
    """
    Maximum Cut Thresholding (MCut)
    Largeron, C., Moulin, C., & Gery, M. (2012). MCut: A Thresholding Strategy
     for Multi-label Classification. In 11th International Symposium, IDA 2012
     (pp. 172-183).
    """
    sorted_probs = probs[probs.argsort()[::-1]]
    difs = sorted_probs[:-1] - sorted_probs[1:]
    t = difs.argmax()
    thresh = (sorted_probs[t] + sorted_probs[t + 1]) / 2
    return thresh


class Predictor:
    def __init__(self):
        self.model_target_size = None
        self.last_loaded_repo = None

    def download_model(self, model_repo):
        csv_path = huggingface_hub.hf_hub_download(
            model_repo,
            LABEL_FILENAME,
        )
        model_path = huggingface_hub.hf_hub_download(
            model_repo,
            MODEL_FILENAME,
        )
        return csv_path, model_path
        
    def load_model(self, model_repo):
        if model_repo == self.last_loaded_repo:
            return

        csv_path, model_path = self.download_model(model_repo)

        tags_df = pd.read_csv(csv_path)
        sep_tags = load_labels(tags_df)

        self.tag_names = sep_tags[0]
        self.rating_indexes = sep_tags[1]
        self.general_indexes = sep_tags[2]
        self.character_indexes = sep_tags[3]

        model = rt.InferenceSession(model_path)
        _, height, width, _ = model.get_inputs()[0].shape
        self.model_target_size = height

        self.last_loaded_repo = model_repo
        self.model = model

    def prepare_image(self, image):
        target_size = self.model_target_size

        canvas = Image.new("RGBA", image.size, (255, 255, 255))
        canvas.alpha_composite(image)
        image = canvas.convert("RGB")

        # Pad image to square
        image_shape = image.size
        max_dim = max(image_shape)
        pad_left = (max_dim - image_shape[0]) // 2
        pad_top = (max_dim - image_shape[1]) // 2

        padded_image = Image.new("RGB", (max_dim, max_dim), (255, 255, 255))
        padded_image.paste(image, (pad_left, pad_top))

        # Resize
        if max_dim != target_size:
            padded_image = padded_image.resize(
                (target_size, target_size),
                Image.BICUBIC,
            )

        # Convert to numpy array
        image_array = np.asarray(padded_image, dtype=np.float32)

        # Convert PIL-native RGB to BGR
        image_array = image_array[:, :, ::-1]

        return np.expand_dims(image_array, axis=0)

        
    def predict(
        self,
        image,
        model_repo,
        general_thresh,
        general_mcut_enabled,
        character_thresh,
        character_mcut_enabled,
    ):
        self.load_model(model_repo)

        image = self.prepare_image(image)

        input_name = self.model.get_inputs()[0].name
        label_name = self.model.get_outputs()[0].name
        preds = self.model.run([label_name], {input_name: image})[0]

        labels = list(zip(self.tag_names, preds[0].astype(float)))

        # First 4 labels are actually ratings: pick one with argmax
        ratings_names = [labels[i] for i in self.rating_indexes]
        rating = dict(ratings_names)

        # Then we have general tags: pick any where prediction confidence > threshold
        general_names = [labels[i] for i in self.general_indexes]

        if general_mcut_enabled:
            general_probs = np.array([x[1] for x in general_names])
            general_thresh = mcut_threshold(general_probs)

        general_res = [x for x in general_names if x[1] > general_thresh]
        general_res = dict(general_res)

        # Everything else is characters: pick any where prediction confidence > threshold
        character_names = [labels[i] for i in self.character_indexes]

        if character_mcut_enabled:
            character_probs = np.array([x[1] for x in character_names])
            character_thresh = mcut_threshold(character_probs)
            character_thresh = max(0.15, character_thresh)

        character_res = [x for x in character_names if x[1] > character_thresh]
        character_res = dict(character_res)

        sorted_general_strings = sorted(
            general_res.items(),
            key=lambda x: x[1],
            reverse=True,
        )
        sorted_general_strings = [x[0] for x in sorted_general_strings]
        sorted_general_strings = (
            ", ".join(sorted_general_strings).replace("(", "\(").replace(")", "\)")
        )

        return sorted_general_strings, rating, character_res, general_res



args = parse_args()
predictor = Predictor()

dropdown_list = [
    SWINV2_MODEL_DSV3_REPO,
    CONV_MODEL_DSV3_REPO,
    VIT_MODEL_DSV3_REPO,
    VIT_LARGE_MODEL_DSV3_REPO,
    EVA02_LARGE_MODEL_DSV3_REPO,
    MOAT_MODEL_DSV2_REPO,
    SWIN_MODEL_DSV2_REPO,
    CONV_MODEL_DSV2_REPO,
    CONV2_MODEL_DSV2_REPO,
    VIT_MODEL_DSV2_REPO,
]

with gr.Blocks(css= "style.css") as app:
    # Dropdown untuk memilih model di luar tab dengan lebar kecil
    with gr.Row():
        model_dropdown = gr.Dropdown(choices=models, label="Model", value="Model B")
        vae_dropdown = gr.Dropdown(choices=vae, label="VAE", value="VAE C")

    # Prompt dan Neg Prompt
    with gr.Row():
        with gr.Column(scale=1):  # Scale 1 ensures full width
            prompt_input = gr.Textbox(label="Prompt", placeholder="Masukkan prompt teks", lines=2, elem_id="prompt-input")
            neg_prompt_input = gr.Textbox(label="Neg Prompt", placeholder="Masukkan negasi prompt", lines=2, elem_id="neg-prompt-input")
           
        generate_button = gr.Button("Generate", elem_id="generate-button", scale=0.13)

    
    # Tab untuk Text-to-Image
    with gr.Tab("Text-to-Image"):
        
        with gr.Row():
            with gr.Column():
                # Konfigurasi
                scheduler_input = gr.Dropdown(choices=schedulers, label="Sampling method", value=schedulers[0])
                num_steps_input = gr.Slider(minimum=1, maximum=100, step=1, label="Sampling steps", value=20)
                width_input = gr.Slider(minimum=128, maximum=2048, step=128, label="Width", value=512)
                height_input = gr.Slider(minimum=128, maximum=2048, step=128, label="Height", value=512)
                cfg_scale_input = gr.Slider(minimum=1, maximum=20, step=1, label="CFG Scale", value=7)
                seed_input = gr.Number(label="Seed", value=-1)
                batch_size = gr.Slider(minimum=1, maximum=24, step=1, label="Batch size", value=1)
                batch_count = gr.Slider(minimum=1, maximum=24, step=1, label="Batch Count", value=1)
            
            
            with gr.Column():
                # Gallery untuk output gambar
                output_gallery = gr.Gallery(label="Hasil Gambar")
                # Output teks JSON di bawah gallery
                output_text = gr.Textbox(label="Output JSON", placeholder="Hasil dalam format JSON", lines=2)

        def update_images(prompt, neg_prompt, width, height, scheduler, num_steps, num_images, cfg_scale, seed, model):
            # Update fungsi sesuai kebutuhan
            return generate_image(prompt, neg_prompt, width, height, scheduler, num_steps, num_images, cfg_scale, seed, model)
        
        generate_button.click(fn=update_images, inputs=[prompt_input, neg_prompt_input, width_input, height_input, scheduler_input, num_steps_input, batch_size, batch_count, cfg_scale_input, seed_input, model_dropdown, vae_dropdown], outputs=[output_gallery, output_text])

    # Tab untuk Image-to-Image
    with gr.Tab("Image-to-Image"):
        with gr.Row():
            with gr.Column():
                image_input = gr.Image(label="Unggah Gambar")
                prompt_input_i2i = gr.Textbox(label="Prompt", placeholder="Masukkan prompt teks", lines=2)
                neg_prompt_input_i2i = gr.Textbox(label="Neg Prompt", placeholder="Masukkan negasi prompt", lines=2)
                generate_button_i2i = gr.Button("Proses Gambar")
            
            with gr.Column():
                output_image_i2i = gr.Image(label="Hasil Gambar")
        
        def process_image_func(image, prompt, neg_prompt, model):
            # Update fungsi sesuai kebutuhan
            return process_image(image, prompt, neg_prompt, model)
        
        generate_button_i2i.click(fn=process_image_func, inputs=[image_input, prompt_input_i2i, neg_prompt_input_i2i, model_dropdown, vae_dropdown], outputs=output_image_i2i)

    # Tab untuk ControlNet
    with gr.Tab("ControlNet"):
        with gr.Row():
            with gr.Column():
                controlnet_dropdown = gr.Dropdown(choices=controlnet_types, label="Pilih Tipe ControlNet")
                controlnet_image_input = gr.Image(label="Unggah Gambar untuk ControlNet")
                controlnet_button = gr.Button("Proses dengan ControlNet")
            
            with gr.Column():
                controlnet_output_image = gr.Image(label="Hasil ControlNet")
        controlnet_button.click(fn=controlnet_process_func, inputs=[controlnet_image_input, controlnet_dropdown, model_dropdown, vae_dropdown], outputs=controlnet_output_image)

    # Tab untuk Intpainting
    with gr.Tab ("Inpainting"):
        with gr.Row():
            with gr.Column():
                image = gr.ImageMask(sources=["upload"], layers=False, transforms=[], format="png", label="base image", show_label=True)
                btn = gr.Button("Inpaint!", elem_id="run_button")
                prompt = gr.Textbox(placeholder="Your prompt (what you want in place of what is erased)", show_label=False, elem_id="prompt")
                negative_prompt = gr.Textbox(label="negative_prompt", placeholder="Your negative prompt", info="what you don't want to see in the image")
                guidance_scale = gr.Number(value=7.5, minimum=1.0, maximum=20.0, step=0.1, label="guidance_scale")
                steps = gr.Number(value=20, minimum=10, maximum=30, step=1, label="steps")
                strength = gr.Number(value=0.99, minimum=0.01, maximum=1.0, step=0.01, label="strength")
                scheduler = gr.Dropdown(label="Schedulers", choices=schedulers, value="EulerDiscreteScheduler")
            with gr.Column():
                image_out = gr.Image(label="Output", elem_id="output-img")
                   
    btn.click(fn=intpaint_func, inputs=[image, prompt, negative_prompt, guidance_scale, steps, strength, scheduler], outputs=[image_out])
    


    
    # Tab untuk Describe
    with gr.Tab("Describe"):
        with gr.Row():
            with gr.Column():
                # Components
                image = gr.Image(type="pil", image_mode="RGBA", label="Input")
                submit_button = gr.Button(value="Submit", variant="primary", size="lg")
                model_repo = gr.Dropdown(dropdown_list, value=SWINV2_MODEL_DSV3_REPO, label="Model")
                general_thresh = gr.Slider(0, 1, step=args.score_slider_step, value=args.score_general_threshold, label="General Tags Threshold", scale=3)
                general_mcut_enabled = gr.Checkbox(value=False, label="Use MCut threshold", scale=1)
                character_thresh = gr.Slider(0, 1, step=args.score_slider_step, value=args.score_character_threshold, label="Character Tags Threshold", scale=3)
                character_mcut_enabled = gr.Checkbox(value=False, label="Use MCut threshold", scale=1)
                clear_button = gr.ClearButton(components=[image, model_repo, general_thresh, general_mcut_enabled, character_thresh, character_mcut_enabled], variant="secondary", size="lg")
                
            with gr.Column():
                sorted_general_strings = gr.Textbox(label="Output (string)")
                rating = gr.Label(label="Rating")
                character_res = gr.Label(label="Output (characters)")
                general_res = gr.Label(label="Output (tags)")
        
        clear_button.add([sorted_general_strings, rating, character_res, general_res])
        submit_button.click(predictor.predict, inputs=[image, model_repo, general_thresh, general_mcut_enabled, character_thresh, character_mcut_enabled], outputs=[sorted_general_strings, rating, character_res, general_res])

# Jalankan antarmuka
app.launch()