import gradio as gr import os import torch import modules.wdtagger from modules.model import get_model_and_vae_options from modules.text2img import generate_image_wrapper # Mendapatkan daftar model dan VAE untuk dropdown all_models, all_vaes = get_model_and_vae_options() # 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) def intpaint_func (image, controlnet_type, model): # Update fungsi sesuai kebutuhan return controlnet_process(image, controlnet_type, model) with gr.Blocks(css="style.css") as app: # Dropdown untuk memilih model di luar tab dengan lebar kecil with gr.Row(): checkpoint_dropdown = gr.Dropdown(choices=all_models, label="Model", value=all_models[0]) vae_dropdown = gr.Dropdown(choices=all_vaes, label="VAE", value=all_vaes[0]) # Tab untuk Text-to-Image with gr.Tab("Text-to-Image"): with gr.Row(): with gr.Column(scale=1): prompt_input = gr.Textbox(label="Prompt", placeholder="Enter Prompt", lines=2, elem_id="prompt-input") neg_prompt_input = gr.Textbox(label="Negative prompt", placeholder="Enter Negative Prompt (optional)", lines=2, elem_id="neg-prompt-input") generate_button = gr.Button("Generate", elem_id="generate-button", scale=0.13) with gr.Row(): with gr.Column(): with gr.Row(): scheduler_input = gr.Dropdown(choices=schedulers, label="Sampling method", value=schedulers[0]) seed_input = gr.Number(label="Seed", value=-1) with gr.Row(): width_input = gr.Slider(minimum=128, maximum=2048, step=128, label="Width", value=1024) batch_size = gr.Slider(minimum=1, maximum=24, step=1, label="Batch size", value=1) with gr.Row(): height_input = gr.Slider(minimum=128, maximum=2048, step=128, label="Height", value=1024) batch_count = gr.Slider(minimum=1, maximum=24, step=1, label="Batch Count", value=1) with gr.Row(): num_steps_input = gr.Slider(minimum=1, maximum=100, step=1, label="Sampling steps", value=20) cfg_scale_input = gr.Slider(minimum=1, maximum=20, step=1, label="CFG Scale", value=7) with gr.Accordion("Hires. fix", open=False): use_hires = gr.Checkbox(label="Use Hires?", value=False, scale=0) with gr.Row(): 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(): 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="Image Results") # Output teks JSON di bawah gallery output_text = gr.Textbox(label="Metadata", placeholder="Results are in Json format", lines=2) def update_images(prompt, neg_prompt, width, height, scheduler, num_steps, batch_size, batch_count, cfg_scale, seed, model, vae): # Update fungsi sesuai kebutuhan return generate_image_wrapper(prompt, neg_prompt, width, height, scheduler, num_steps, batch_size, batch_count, cfg_scale, seed, model, vae) 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, checkpoint_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(scale=1): prompt_input_i2i = gr.Textbox(label="Prompt", placeholder="Masukkan prompt teks", lines=2, elem_id="prompt-input") neg_prompt_input_i2i = 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) with gr.Row(): with gr.Column(): image_input = gr.Image(label="Unggah Gambar") generate_button_i2i = gr.Button("Generate") with gr.Row(): scheduler_input = gr.Dropdown(choices=schedulers, label="Sampling method", value=schedulers[0]) seed_input = gr.Number(label="Seed", value=-1) with gr.Row(): steps = gr.Slider(minimum=1, maximum=100, step=1, label="Steps", value=20) cfg_scale = gr.Slider(minimum=1, maximum=24, step=1, label="CFG Scale", value=7) with gr.Row(): strength = gr.Slider(minimum=0, maximum=1, step=0.1, label="Strength", value=0.6) 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, checkpoint_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, checkpoint_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(modules.wdtagger.dropdown_list, value=modules.wdtagger.dropdown_list[0], label="Model") general_thresh = gr.Slider(0, 1, step=modules.wdtagger.args.score_slider_step, value=modules.wdtagger.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=modules.wdtagger.args.score_slider_step, value=modules.wdtagger.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(modules.wdtagger.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()