Upload app (12).py

app (12).py

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+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()