old2/tabs/image_tab.py

# tabs/image_tab.py
import random

import gradio as gr
from modules.helpers.common_helpers import *
from modules.events.common_events import *


def image_tab():
    with gr.Tabs():
        with gr.Tab("Flux"):
            flux_tab()
        with gr.Tab("SDXL"):
            sdxl_tab()


def flux_tab():
    from modules.events.flux_events import (
        update_fast_generation,
        add_to_enabled_loras,
        generate_image
    )
    from config import flux_models, flux_loras
    
    loras = flux_loras
    with gr.Row():
        with gr.Column():
            with gr.Group() as image_options:
                model = gr.Dropdown(label="Models", choices=flux_models, value=flux_models[0], interactive=True)
                prompt = gr.Textbox(lines=5, label="Prompt")
                fast_generation = gr.Checkbox(label="Fast Generation (Hyper-SD) 🧪")
            
            
            with gr.Accordion("Loras", open=True): # Lora Gallery
                lora_gallery = gr.Gallery(
                    label="Gallery",
                    value=[(lora['image'], lora['title']) for lora in loras],
                    allow_preview=False,
                    columns=3,
                    rows=3,
                    type="pil"
                )
                
                with gr.Group():
                    with gr.Column():
                        with gr.Row():
                            custom_lora = gr.Textbox(label="Custom Lora", info="Enter a Huggingface repo path")
                            selected_lora = gr.Textbox(label="Selected Lora", info="Choose from the gallery or enter a custom LoRA")
                        
                        custom_lora_info = gr.HTML(visible=False)
                        add_lora = gr.Button(value="Add LoRA")
                        
                        enabled_loras = gr.State(value=[])
                        with gr.Group():
                            with gr.Row():
                                for i in range(6): # only support max 6 loras due to inference time
                                    with gr.Column():
                                        with gr.Column(scale=2):
                                            globals()[f"lora_slider_{i}"] = gr.Slider(label=f"LoRA {i+1}", minimum=0, maximum=1, step=0.01, value=0.8, visible=False, interactive=True)
                                        with gr.Column():
                                            globals()[f"lora_remove_{i}"] = gr.Button(value="Remove LoRA", visible=False) 

            
            with gr.Accordion("Embeddings", open=False): # Embeddings
                gr.Label("To be implemented")
            
            
            with gr.Accordion("Image Options", open=False): # Image Options
                with gr.Tabs():
                    image_options = {
                        "img2img": "Upload Image",
                        "inpaint": "Upload Image",
                        "canny": "Upload Image",
                        "pose": "Upload Image",
                        "depth": "Upload Image",
                    }
                    
                    for image_option, label in image_options.items():
                        with gr.Tab(image_option):
                            if not image_option in ['inpaint', 'scribble']:
                                globals()[f"{image_option}_image"] = gr.Image(label=label, type="pil")
                            elif image_option in ['inpaint', 'scribble']:
                                globals()[f"{image_option}_image"] = gr.ImageEditor(
                                    label=label,
                                    image_mode='RGB',
                                    layers=False,
                                    brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed") if image_option == 'inpaint' else gr.Brush(),
                                    interactive=True,
                                    type="pil",
                                )
                            
                            # Image Strength (Co-relates to controlnet strength, strength for img2img n inpaint)
                            globals()[f"{image_option}_strength"] = gr.Slider(label="Strength", minimum=0, maximum=1, step=0.01, value=1.0, interactive=True)
                    
                    resize_mode = gr.Radio(
                        label="Resize Mode",
                        choices=["crop and resize", "resize only", "resize and fill"],
                        value="resize and fill",
                        interactive=True
                    )
        
        
        with gr.Column():
            with gr.Group():
                output_images = gr.Gallery(
                        label="Output Images",
                        value=[],
                        allow_preview=True,
                        type="pil",
                        interactive=False,
                    )
                generate_images = gr.Button(value="Generate Images", variant="primary")            
            
            with gr.Accordion("Advance Settings", open=True):
                with gr.Row():
                    scheduler = gr.Dropdown(
                        label="Scheduler",
                        choices = [
                            "fm_euler"
                        ],
                        value="fm_euler",
                        interactive=True
                    )

                with gr.Row():
                    for column in range(2):
                        with gr.Column():
                            options = [
                                ("Height", "image_height", 64, 2048, 64, 1024, True),
                                ("Width", "image_width", 64, 2048, 64, 1024, True),
                                ("Num Images Per Prompt", "image_num_images_per_prompt", 1, 4, 1, 1, True),
                                ("Num Inference Steps", "image_num_inference_steps", 1, 100, 1, 20, True),
                                ("Clip Skip", "image_clip_skip", 0, 2, 1, 2, False),
                                ("Guidance Scale", "image_guidance_scale", 0, 20, 0.5, 3.5, True),
                                ("Seed", "image_seed", 0, 100000, 1, random.randint(0, 100000), True),
                            ]
                            for label, var_name, min_val, max_val, step, value, visible in options[column::2]:
                                globals()[var_name] = gr.Slider(label=label, minimum=min_val, maximum=max_val, step=step, value=value, visible=visible, interactive=True)
                
                with gr.Row():
                    refiner = gr.Checkbox(
                        label="Refiner 🧪",
                        value=False,
                    )
                    vae = gr.Checkbox(
                        label="VAE",
                        value=True,
                    )
    
    # Events
    # Base Options
    fast_generation.change(update_fast_generation, [fast_generation], [image_guidance_scale, image_num_inference_steps]) # Fast Generation # type: ignore
    

    # Lora Gallery
    lora_gallery.select(selected_lora_from_gallery, None, selected_lora)
    custom_lora.change(update_selected_lora, custom_lora, [selected_lora, custom_lora_info])
    add_lora.click(add_to_enabled_loras, [selected_lora, enabled_loras], [selected_lora, custom_lora_info, enabled_loras])
    enabled_loras.change(update_lora_sliders, enabled_loras, [lora_slider_0, lora_slider_1, lora_slider_2, lora_slider_3, lora_slider_4, lora_slider_5, lora_remove_0, lora_remove_1, lora_remove_2, lora_remove_3, lora_remove_4, lora_remove_5]) # type: ignore

    for i in range(6):
        globals()[f"lora_remove_{i}"].click(
            lambda enabled_loras, index=i: remove_from_enabled_loras(enabled_loras, index),
            [enabled_loras],
            [enabled_loras]
        )
    

    # Generate Image
    generate_images.click(
        generate_image, # type: ignore
        [
            model, prompt, fast_generation, enabled_loras,
            lora_slider_0, lora_slider_1, lora_slider_2, lora_slider_3, lora_slider_4, lora_slider_5, # type: ignore
            img2img_image, inpaint_image, canny_image, pose_image, depth_image, # type: ignore
            img2img_strength, inpaint_strength, canny_strength, pose_strength, depth_strength, # type: ignore
            resize_mode,
            scheduler, image_height, image_width, image_num_images_per_prompt, # type: ignore
            image_num_inference_steps, image_guidance_scale, image_seed, # type: ignore
            refiner, vae
        ],
        [output_images]
    )


def sdxl_tab():
    from modules.events.sdxl_events import (
        update_fast_generation,
        add_to_enabled_loras,
    )
    from config import sdxl_models, sdxl_loras
    
    loras = sdxl_loras
    with gr.Row():
        with gr.Column():
            with gr.Group() as image_options:
                model = gr.Dropdown(label="Models", choices=sdxl_models, value=sdxl_models[0], interactive=True)
                prompt = gr.Textbox(lines=5, label="Prompt")
                negative_prompt = gr.Textbox(lines=5, label="Negative Prompt")
                fast_generation = gr.Checkbox(label="Fast Generation (Hyper-SD) 🧪")
            
            
            with gr.Accordion("Loras", open=True):
                lora_gallery = gr.Gallery(
                    label="Gallery",
                    value=[(lora['image'], lora['title']) for lora in loras],
                    allow_preview=False,
                    columns=3,
                    rows=3,
                    type="pil"
                )
                
                with gr.Group():
                    with gr.Column():
                        with gr.Row():
                            custom_lora = gr.Textbox(label="Custom Lora", info="Enter a Huggingface repo path")
                            selected_lora = gr.Textbox(label="Selected Lora", info="Choose from the gallery or enter a custom LoRA")
                        
                        custom_lora_info = gr.HTML(visible=False)
                        add_lora = gr.Button(value="Add LoRA")
                        
                        enabled_loras = gr.State(value=[])
                        with gr.Group():
                            with gr.Row():
                                for i in range(6):
                                    with gr.Column():
                                        with gr.Column(scale=2):
                                            globals()[f"lora_slider_{i}"] = gr.Slider(label=f"LoRA {i+1}", minimum=0, maximum=1, step=0.01, value=0.8, visible=False, interactive=True)
                                        with gr.Column():
                                            globals()[f"lora_remove_{i}"] = gr.Button(value="Remove LoRA", visible=False)
                                            
            with gr.Accordion("Embeddings", open=False):
                custom_embedding = gr.Textbox(label="Custom Embedding")
                custom_embedding_info = gr.HTML(visible=False)
                add_embedding = gr.Button(value="Add Embedding")
                enabled_embeddings = gr.State(value=[])
                with gr.Group():
                    with gr.Row():
                        for i in range(6):
                            with gr.Column():
                                with gr.Column(scale=2):
                                    globals()[f"embedding_list_{i}"] = gr.Label(label=f"Embedding {i+1}", visible=False)
                                with gr.Column():
                                    globals()[f"embedding_remove_{i}"] = gr.Button(value="Remove Embedding", visible=False)
            
            with gr.Accordion("Image Options", open=False):
                with gr.Tabs():
                    image_options = {
                        "img2img": "Upload Image",
                        "inpaint": "Upload Image",
                        "canny": "Upload Image",
                        "pose": "Upload Image",
                        "depth": "Upload Image",
                        "scribble": "Upload Image",
                    }
                    
                    for image_option, label in image_options.items():
                        with gr.Tab(image_option):
                            if not image_option in ['inpaint', 'scribble']:
                                globals()[f"{image_option}_image"] = gr.Image(label=label, type="pil")
                            elif image_option in ['inpaint', 'scribble']:
                                globals()[f"{image_option}_image"] = gr.ImageEditor(
                                    label=label,
                                    image_mode='RGB',
                                    layers=False,
                                    brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed") if image_option == 'inpaint' else gr.Brush(),
                                    interactive=True,
                                    type="pil",
                                )
                            
                            globals()[f"{image_option}_strength"] = gr.Slider(label="Strength", minimum=0, maximum=1, step=0.01, value=1.0, interactive=True)
                    
                    resize_mode = gr.Radio(
                        label="Resize Mode",
                        choices=["crop and resize", "resize only", "resize and fill"],
                        value="resize and fill",
                        interactive=True
                    )
        
        with gr.Column():
            with gr.Group():
                output_images = gr.Gallery(
                        label="Output Images",
                        value=[],
                        allow_preview=True,
                        type="pil",
                        interactive=False,
                    )
                generate_images = gr.Button(value="Generate Images", variant="primary")
            
            with gr.Accordion("Advance Settings", open=True):
                with gr.Row():
                    scheduler = gr.Dropdown(
                        label="Scheduler",
                        choices = [
                            "dpmpp_2m", "dpmpp_2m_k", "dpmpp_2m_sde", "dpmpp_2m_sde_k",
                            "dpmpp_sde", "dpmpp_sde_k", "dpm2", "dpm2_k", "dpm2_a",
                            "dpm2_a_k", "euler", "euler_a", "heun", "lms", "lms_k",
                            "deis", "unipc"
                        ],
                        value="dpmpp_2m_sde_k",
                        interactive=True
                    )

                with gr.Row():
                    for column in range(2):
                        with gr.Column():
                            options = [
                                ("Height", "image_height", 64, 2048, 64, 1024, True),
                                ("Width", "image_width", 64, 2048, 64, 1024, True),
                                ("Num Images Per Prompt", "image_num_images_per_prompt", 1, 4, 1, 1, True),
                                ("Num Inference Steps", "image_num_inference_steps", 1, 100, 1, 20, True),
                                ("Clip Skip", "image_clip_skip", 0, 2, 1, 2, True),
                                ("Guidance Scale", "image_guidance_scale", 0, 20, 0.5, 7.0, True),
                                ("Seed", "image_seed", 0, 100000, 1, random.randint(0, 100000), True),
                            ]
                            for label, var_name, min_val, max_val, step, value, visible in options[column::2]:
                                globals()[var_name] = gr.Slider(label=label, minimum=min_val, maximum=max_val, step=step, value=value, visible=visible, interactive=True)
                
                with gr.Row():
                    refiner = gr.Checkbox(
                        label="Refiner 🧪",
                        value=False,
                    )
                    vae = gr.Checkbox(
                        label="VAE",
                        value=True,
                    )
    
    # Events
    # Base Options
    fast_generation.change(update_fast_generation, [fast_generation], [image_guidance_scale, image_num_inference_steps]) # Fast Generation # type: ignore
    
    
    # Lora Gallery
    lora_gallery.select(selected_lora_from_gallery, None, selected_lora)
    custom_lora.change(update_selected_lora, custom_lora, [selected_lora, custom_lora_info])
    add_lora.click(add_to_enabled_loras, [selected_lora, enabled_loras], [selected_lora, custom_lora_info, enabled_loras])
    enabled_loras.change(update_lora_sliders, enabled_loras, [lora_slider_0, lora_slider_1, lora_slider_2, lora_slider_3, lora_slider_4, lora_slider_5, lora_remove_0, lora_remove_1, lora_remove_2, lora_remove_3, lora_remove_4, lora_remove_5]) # type: ignore
    
    for i in range(6):
        globals()[f"lora_remove_{i}"].click(
            lambda enabled_loras, index=i: remove_from_enabled_loras(enabled_loras, index),
            [enabled_loras],
            [enabled_loras]
        )

    
    # Embeddings
    custom_embedding.change(update_custom_embedding, custom_embedding, [custom_embedding_info])
    add_embedding.click(add_to_embeddings, [custom_embedding, enabled_embeddings], [custom_embedding, custom_embedding_info, enabled_embeddings])
    for i in range(6):
        globals()[f"embedding_remove_{i}"].click(
            lambda enabled_embeddings, index=i: remove_from_embeddings(enabled_embeddings, index),
            [enabled_embeddings],
            [enabled_embeddings]
        )
    
    # Generate Image
    generate_images.click(
        generate_image, # type: ignore
        [
            model, prompt, negative_prompt, fast_generation, enabled_loras, enabled_embeddings,
            lora_slider_0, lora_slider_1, lora_slider_2, lora_slider_3, lora_slider_4, lora_slider_5, # type: ignore
            img2img_image, inpaint_image, canny_image, pose_image, depth_image, scribble_image, # type: ignore
            img2img_strength, inpaint_strength, canny_strength, pose_strength, depth_strength, scribble_strength, # type: ignore
            resize_mode,
            scheduler, image_height, image_width, image_num_images_per_prompt, # type: ignore
            image_num_inference_steps, image_clip_skip, image_guidance_scale, image_seed, # type: ignore
            refiner, vae
        ],
        [output_images]
    )