Delete app.py

app.py

@@ -1,378 +0,0 @@
-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.Accordion("Hires. fix"):
-                    use_hires = gr.Checkbox(label="Use Hires?", value=False, scale=0)
-                    with gr.Row(scale=1):
-                        upscaler = gr.Dropdown(choices=schedulers, label="Upscaler", value=schedulers[0])
-                        upscale_by = gr.Slider(minimum=1, maximum=8, step=1, label="Upscale by", value=2)
-                    with gr.Row(scale=0.18):
-                        hires_steps = gr.Slider(minimum=1, maximum=50, step=1, label="Hires Steps", value=20)
-                        denois_strength = gr.Slider(minimum=0, maximum=1, step=0.02, label="Denoising Strength", value=2)
-            
-            
-            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()