text-generation-webui/modules/ui_image_generation.py

import json
import os
import time
import traceback
from datetime import datetime
from pathlib import Path

import gradio as gr
import numpy as np
from PIL import Image
from PIL.PngImagePlugin import PngInfo

from modules import shared, ui, utils
from modules.image_models import load_image_model, unload_image_model
from modules.logging_colors import logger
from modules.utils import gradio

ASPECT_RATIOS = {
    "1:1 Square": (1, 1),
    "16:9 Cinema": (16, 9),
    "9:16 Mobile": (9, 16),
    "4:3 Photo": (4, 3),
    "Custom": None,
}

STEP = 16
IMAGES_PER_PAGE = 64

# Settings keys to save in PNG metadata (Generate tab only)
METADATA_SETTINGS_KEYS = [
    'image_prompt',
    'image_neg_prompt',
    'image_width',
    'image_height',
    'image_aspect_ratio',
    'image_steps',
    'image_seed',
    'image_batch_size',
    'image_batch_count',
]

# Cache for all image paths
_image_cache = []
_cache_timestamp = 0


def round_to_step(value, step=STEP):
    return round(value / step) * step


def clamp(value, min_val, max_val):
    return max(min_val, min(max_val, value))


def apply_aspect_ratio(aspect_ratio, current_width, current_height):
    if aspect_ratio == "Custom" or aspect_ratio not in ASPECT_RATIOS:
        return current_width, current_height

    w_ratio, h_ratio = ASPECT_RATIOS[aspect_ratio]

    if w_ratio == h_ratio:
        base = min(current_width, current_height)
        new_width = base
        new_height = base
    elif w_ratio < h_ratio:
        new_width = current_width
        new_height = round_to_step(current_width * h_ratio / w_ratio)
    else:
        new_height = current_height
        new_width = round_to_step(current_height * w_ratio / h_ratio)

    new_width = clamp(new_width, 256, 2048)
    new_height = clamp(new_height, 256, 2048)

    return int(new_width), int(new_height)


def update_height_from_width(width, aspect_ratio):
    if aspect_ratio == "Custom" or aspect_ratio not in ASPECT_RATIOS:
        return gr.update()

    w_ratio, h_ratio = ASPECT_RATIOS[aspect_ratio]
    new_height = round_to_step(width * h_ratio / w_ratio)
    new_height = clamp(new_height, 256, 2048)

    return int(new_height)


def update_width_from_height(height, aspect_ratio):
    if aspect_ratio == "Custom" or aspect_ratio not in ASPECT_RATIOS:
        return gr.update()

    w_ratio, h_ratio = ASPECT_RATIOS[aspect_ratio]
    new_width = round_to_step(height * w_ratio / h_ratio)
    new_width = clamp(new_width, 256, 2048)

    return int(new_width)


def swap_dimensions_and_update_ratio(width, height, aspect_ratio):
    new_width, new_height = height, width

    new_ratio = "Custom"
    for name, ratios in ASPECT_RATIOS.items():
        if ratios is None:
            continue
        w_r, h_r = ratios
        expected_height = new_width * h_r / w_r
        if abs(expected_height - new_height) < STEP:
            new_ratio = name
            break

    return new_width, new_height, new_ratio


def build_generation_metadata(state, actual_seed):
    """Build metadata dict from generation settings."""
    metadata = {}
    for key in METADATA_SETTINGS_KEYS:
        if key in state:
            metadata[key] = state[key]

    # Store the actual seed used (not -1)
    metadata['image_seed'] = actual_seed
    metadata['generated_at'] = datetime.now().isoformat()
    metadata['model'] = shared.image_model_name

    return metadata


def save_generated_images(images, state, actual_seed):
    """Save images with generation metadata embedded in PNG."""
    date_str = datetime.now().strftime("%Y-%m-%d")
    folder_path = os.path.join("user_data", "image_outputs", date_str)
    os.makedirs(folder_path, exist_ok=True)

    metadata = build_generation_metadata(state, actual_seed)
    metadata_json = json.dumps(metadata, ensure_ascii=False)

    for idx, img in enumerate(images):
        timestamp = datetime.now().strftime("%H-%M-%S")
        filename = f"{timestamp}_{actual_seed:010d}_{idx:03d}.png"
        filepath = os.path.join(folder_path, filename)

        # Create PNG metadata
        png_info = PngInfo()
        png_info.add_text("image_gen_settings", metadata_json)

        # Save with metadata
        img.save(filepath, pnginfo=png_info)


def read_image_metadata(image_path):
    """Read generation metadata from PNG file."""
    try:
        with Image.open(image_path) as img:
            if hasattr(img, 'text') and 'image_gen_settings' in img.text:
                return json.loads(img.text['image_gen_settings'])
    except Exception as e:
        logger.debug(f"Could not read metadata from {image_path}: {e}")
    return None


def format_metadata_for_display(metadata):
    """Format metadata as readable text."""
    if not metadata:
        return "No generation settings found in this image."

    lines = ["**Generation Settings**", ""]

    # Display in a nice order
    display_order = [
        ('image_prompt', 'Prompt'),
        ('image_neg_prompt', 'Negative Prompt'),
        ('image_width', 'Width'),
        ('image_height', 'Height'),
        ('image_aspect_ratio', 'Aspect Ratio'),
        ('image_steps', 'Steps'),
        ('image_seed', 'Seed'),
        ('image_batch_size', 'Batch Size'),
        ('image_batch_count', 'Batch Count'),
        ('model', 'Model'),
        ('generated_at', 'Generated At'),
    ]

    for key, label in display_order:
        if key in metadata:
            value = metadata[key]
            if key in ['image_prompt', 'image_neg_prompt'] and value:
                # Truncate long prompts for display
                if len(str(value)) > 200:
                    value = str(value)[:200] + "..."
            lines.append(f"**{label}:** {value}")

    return "\n\n".join(lines)


def get_all_history_images(force_refresh=False):
    """Get all history images sorted by modification time (newest first). Uses caching."""
    global _image_cache, _cache_timestamp

    output_dir = os.path.join("user_data", "image_outputs")
    if not os.path.exists(output_dir):
        return []

    # Check if we need to refresh cache
    current_time = time.time()
    if not force_refresh and _image_cache and (current_time - _cache_timestamp) < 2:
        return _image_cache

    image_files = []
    for root, _, files in os.walk(output_dir):
        for file in files:
            if file.endswith((".png", ".jpg", ".jpeg")):
                full_path = os.path.join(root, file)
                image_files.append((full_path, os.path.getmtime(full_path)))

    image_files.sort(key=lambda x: x[1], reverse=True)
    _image_cache = [x[0] for x in image_files]
    _cache_timestamp = current_time

    return _image_cache


def get_paginated_images(page=0, force_refresh=False):
    """Get images for a specific page."""
    all_images = get_all_history_images(force_refresh)
    total_images = len(all_images)
    total_pages = max(1, (total_images + IMAGES_PER_PAGE - 1) // IMAGES_PER_PAGE)

    # Clamp page to valid range
    page = max(0, min(page, total_pages - 1))

    start_idx = page * IMAGES_PER_PAGE
    end_idx = min(start_idx + IMAGES_PER_PAGE, total_images)

    page_images = all_images[start_idx:end_idx]

    return page_images, page, total_pages, total_images


def refresh_gallery(current_page=0):
    """Refresh gallery with current page."""
    images, page, total_pages, total_images = get_paginated_images(current_page, force_refresh=True)
    page_info = f"Page {page + 1} of {total_pages} ({total_images} total images)"
    return images, page, page_info


def go_to_page(page_num, current_page):
    """Go to a specific page (1-indexed input)."""
    try:
        page = int(page_num) - 1  # Convert to 0-indexed
    except (ValueError, TypeError):
        page = current_page

    images, page, total_pages, total_images = get_paginated_images(page)
    page_info = f"Page {page + 1} of {total_pages} ({total_images} total images)"
    return images, page, page_info


def next_page(current_page):
    """Go to next page."""
    images, page, total_pages, total_images = get_paginated_images(current_page + 1)
    page_info = f"Page {page + 1} of {total_pages} ({total_images} total images)"
    return images, page, page_info


def prev_page(current_page):
    """Go to previous page."""
    images, page, total_pages, total_images = get_paginated_images(current_page - 1)
    page_info = f"Page {page + 1} of {total_pages} ({total_images} total images)"
    return images, page, page_info


def on_gallery_select(evt: gr.SelectData, current_page):
    """Handle image selection from gallery."""
    if evt.index is None:
        return "", "Select an image to view its settings"

    # Get the current page's images to find the actual file path
    all_images = get_all_history_images()
    total_images = len(all_images)

    # Calculate the actual index in the full list
    start_idx = current_page * IMAGES_PER_PAGE
    actual_idx = start_idx + evt.index

    if actual_idx >= total_images:
        return "", "Image not found"

    image_path = all_images[actual_idx]
    metadata = read_image_metadata(image_path)
    metadata_display = format_metadata_for_display(metadata)

    return image_path, metadata_display


def send_to_generate(selected_image_path):
    """Load settings from selected image and return updates for all Generate tab inputs."""
    if not selected_image_path or not os.path.exists(selected_image_path):
        return [gr.update()] * 9 + ["No image selected"]

    metadata = read_image_metadata(selected_image_path)
    if not metadata:
        return [gr.update()] * 9 + ["No settings found in this image"]

    # Return updates for each input element in order
    updates = [
        gr.update(value=metadata.get('image_prompt', '')),
        gr.update(value=metadata.get('image_neg_prompt', '')),
        gr.update(value=metadata.get('image_width', 1024)),
        gr.update(value=metadata.get('image_height', 1024)),
        gr.update(value=metadata.get('image_aspect_ratio', '1:1 Square')),
        gr.update(value=metadata.get('image_steps', 9)),
        gr.update(value=metadata.get('image_seed', -1)),
        gr.update(value=metadata.get('image_batch_size', 1)),
        gr.update(value=metadata.get('image_batch_count', 1)),
    ]

    status = f"✓ Settings loaded from image (seed: {metadata.get('image_seed', 'unknown')})"
    return updates + [status]



def create_ui():
    if shared.settings['image_model_menu'] != 'None':
        shared.image_model_name = shared.settings['image_model_menu']

    with gr.Tab("Image AI", elem_id="image-ai-tab"):
        with gr.Tabs():
            # TAB 1: GENERATE
            with gr.TabItem("Generate"):
                with gr.Row():
                    with gr.Column(scale=4, min_width=350):
                        shared.gradio['image_prompt'] = gr.Textbox(
                            label="Prompt",
                            placeholder="Describe your imagination...",
                            lines=3,
                            autofocus=True,
                            value=shared.settings['image_prompt']
                        )
                        shared.gradio['image_neg_prompt'] = gr.Textbox(
                            label="Negative Prompt",
                            placeholder="Low quality...",
                            lines=3,
                            value=shared.settings['image_neg_prompt']
                        )

                        shared.gradio['image_generate_btn'] = gr.Button("✨ GENERATE", variant="primary", size="lg", elem_id="gen-btn")
                        gr.HTML("<hr style='border-top: 1px solid #444; margin: 20px 0;'>")

                        gr.Markdown("### Dimensions")
                        with gr.Row():
                            with gr.Column():
                                shared.gradio['image_width'] = gr.Slider(256, 2048, value=shared.settings['image_width'], step=32, label="Width")
                            with gr.Column():
                                shared.gradio['image_height'] = gr.Slider(256, 2048, value=shared.settings['image_height'], step=32, label="Height")
                            shared.gradio['image_swap_btn'] = gr.Button("⇄ Swap", elem_classes='refresh-button', scale=0, min_width=80, elem_id="swap-height-width")

                        with gr.Row():
                            shared.gradio['image_aspect_ratio'] = gr.Radio(
                                choices=["1:1 Square", "16:9 Cinema", "9:16 Mobile", "4:3 Photo", "Custom"],
                                value=shared.settings['image_aspect_ratio'],
                                label="Aspect Ratio",
                                interactive=True
                            )

                        gr.Markdown("### Config")
                        with gr.Row():
                            with gr.Column():
                                shared.gradio['image_steps'] = gr.Slider(1, 100, value=shared.settings['image_steps'], step=1, label="Steps")
                                shared.gradio['image_seed'] = gr.Number(label="Seed", value=shared.settings['image_seed'], precision=0, info="-1 = Random")
                            with gr.Column():
                                shared.gradio['image_batch_size'] = gr.Slider(1, 32, value=shared.settings['image_batch_size'], step=1, label="Batch Size (VRAM Heavy)", info="Generates N images at once.")
                                shared.gradio['image_batch_count'] = gr.Slider(1, 128, value=shared.settings['image_batch_count'], step=1, label="Sequential Count (Loop)", info="Repeats the generation N times.")

                    with gr.Column(scale=6, min_width=500):
                        with gr.Column(elem_classes=["viewport-container"]):
                            shared.gradio['image_output_gallery'] = gr.Gallery(label="Output", show_label=False, columns=2, rows=2, height="80vh", object_fit="contain", preview=True, elem_id="image-output-gallery")

            # TAB 2: GALLERY (with pagination)
            with gr.TabItem("Gallery"):
                with gr.Row():
                    with gr.Column(scale=3):
                        # Pagination controls
                        with gr.Row():
                            shared.gradio['image_refresh_history'] = gr.Button("🔄 Refresh", elem_classes="refresh-button")
                            shared.gradio['image_prev_page'] = gr.Button("◀ Prev", elem_classes="refresh-button")
                            shared.gradio['image_page_info'] = gr.Markdown("Loading...", elem_id="image-page-info")
                            shared.gradio['image_next_page'] = gr.Button("Next ▶", elem_classes="refresh-button")
                            shared.gradio['image_page_input'] = gr.Number(value=1, label="Page", precision=0, minimum=1, scale=0, min_width=80)
                            shared.gradio['image_go_to_page'] = gr.Button("Go", elem_classes="refresh-button", scale=0, min_width=50)

                        # State for current page and selected image path
                        shared.gradio['image_current_page'] = gr.State(value=0)
                        shared.gradio['image_selected_path'] = gr.State(value="")

                        # Paginated gallery using gr.Gallery
                        shared.gradio['image_history_gallery'] = gr.Gallery(
                            value=lambda: get_paginated_images(0)[0],
                            label="Image History",
                            show_label=False,
                            columns=6,
                            object_fit="cover",
                            height="auto",
                            allow_preview=True,
                            elem_id="image-history-gallery"
                        )

                    with gr.Column(scale=1):
                        gr.Markdown("### Selected Image")
                        shared.gradio['image_settings_display'] = gr.Markdown("Select an image to view its settings")
                        shared.gradio['image_send_to_generate'] = gr.Button("📤 Send to Generate", variant="primary")
                        shared.gradio['image_gallery_status'] = gr.Markdown("")

            # TAB 3: MODEL
            with gr.TabItem("Model"):
                with gr.Row():
                    with gr.Column():
                        with gr.Row():
                            shared.gradio['image_model_menu'] = gr.Dropdown(
                                choices=utils.get_available_image_models(),
                                value=shared.settings['image_model_menu'],
                                label='Model',
                                elem_classes='slim-dropdown'
                            )
                            shared.gradio['image_refresh_models'] = gr.Button("🔄", elem_classes='refresh-button', scale=0, min_width=40)
                            shared.gradio['image_load_model'] = gr.Button("Load", variant='primary', elem_classes='refresh-button')
                            shared.gradio['image_unload_model'] = gr.Button("Unload", elem_classes='refresh-button')

                        gr.Markdown("## Settings")
                        with gr.Row():
                            with gr.Column():
                                shared.gradio['image_quant'] = gr.Dropdown(
                                    label='Quantization',
                                    choices=['none', 'bnb-8bit', 'bnb-4bit', 'quanto-8bit', 'quanto-4bit', 'quanto-2bit'],
                                    value=shared.settings['image_quant'],
                                    info='Quantization method for reduced VRAM usage. Quanto supports lower precisions (2-bit, 4-bit, 8-bit).'
                                )

                                shared.gradio['image_dtype'] = gr.Dropdown(
                                    choices=['bfloat16', 'float16'],
                                    value=shared.settings['image_dtype'],
                                    label='Data Type',
                                    info='bfloat16 recommended for modern GPUs'
                                )
                                shared.gradio['image_attn_backend'] = gr.Dropdown(
                                    choices=['sdpa', 'flash_attention_2', 'flash_attention_3'],
                                    value=shared.settings['image_attn_backend'],
                                    label='Attention Backend',
                                    info='SDPA is default. Flash Attention requires compatible GPU.'
                                )
                            with gr.Column():
                                shared.gradio['image_compile'] = gr.Checkbox(
                                    value=shared.settings['image_compile'],
                                    label='Compile Model',
                                    info='Faster inference after first run. First run will be slow.'
                                )
                                shared.gradio['image_cpu_offload'] = gr.Checkbox(
                                    value=shared.settings['image_cpu_offload'],
                                    label='CPU Offload',
                                    info='Enable for low VRAM GPUs. Slower but uses less memory.'
                                )

                    with gr.Column():
                        shared.gradio['image_download_path'] = gr.Textbox(
                            label="Download model",
                            placeholder="Tongyi-MAI/Z-Image-Turbo",
                            info="Enter HuggingFace path. Use : for branch, e.g. user/model:main"
                        )
                        shared.gradio['image_download_btn'] = gr.Button("Download", variant='primary')
                        shared.gradio['image_model_status'] = gr.Markdown(
                            value=f"Model: **{shared.settings['image_model_menu']}** (not loaded)" if shared.settings['image_model_menu'] != 'None' else "No model selected"
                        )


def create_event_handlers():
    # Dimension controls
    shared.gradio['image_aspect_ratio'].change(
        apply_aspect_ratio,
        gradio('image_aspect_ratio', 'image_width', 'image_height'),
        gradio('image_width', 'image_height'),
        show_progress=False
    )

    shared.gradio['image_width'].release(
        update_height_from_width,
        gradio('image_width', 'image_aspect_ratio'),
        gradio('image_height'),
        show_progress=False
    )

    shared.gradio['image_height'].release(
        update_width_from_height,
        gradio('image_height', 'image_aspect_ratio'),
        gradio('image_width'),
        show_progress=False
    )

    shared.gradio['image_swap_btn'].click(
        swap_dimensions_and_update_ratio,
        gradio('image_width', 'image_height', 'image_aspect_ratio'),
        gradio('image_width', 'image_height', 'image_aspect_ratio'),
        show_progress=False
    )

    # Generation
    shared.gradio['image_generate_btn'].click(
        ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
        generate, gradio('interface_state'), gradio('image_output_gallery'))

    shared.gradio['image_prompt'].submit(
        ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
        generate, gradio('interface_state'), gradio('image_output_gallery'))

    shared.gradio['image_neg_prompt'].submit(
        ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
        generate, gradio('interface_state'), gradio('image_output_gallery'))

    # Model management
    shared.gradio['image_refresh_models'].click(
        lambda: gr.update(choices=utils.get_available_image_models()),
        None,
        gradio('image_model_menu'),
        show_progress=False
    )

    shared.gradio['image_load_model'].click(
        load_image_model_wrapper,
        gradio('image_model_menu', 'image_dtype', 'image_attn_backend', 'image_cpu_offload', 'image_compile', 'image_quant'),
        gradio('image_model_status'),
        show_progress=True
    )

    shared.gradio['image_unload_model'].click(
        unload_image_model_wrapper,
        None,
        gradio('image_model_status'),
        show_progress=False
    )

    shared.gradio['image_download_btn'].click(
        download_image_model_wrapper,
        gradio('image_download_path'),
        gradio('image_model_status', 'image_model_menu'),
        show_progress=True
    )

    # Gallery pagination handlers
    shared.gradio['image_refresh_history'].click(
        refresh_gallery,
        gradio('image_current_page'),
        gradio('image_history_gallery', 'image_current_page', 'image_page_info'),
        show_progress=False
    )

    shared.gradio['image_next_page'].click(
        next_page,
        gradio('image_current_page'),
        gradio('image_history_gallery', 'image_current_page', 'image_page_info'),
        show_progress=False
    )

    shared.gradio['image_prev_page'].click(
        prev_page,
        gradio('image_current_page'),
        gradio('image_history_gallery', 'image_current_page', 'image_page_info'),
        show_progress=False
    )

    shared.gradio['image_go_to_page'].click(
        go_to_page,
        gradio('image_page_input', 'image_current_page'),
        gradio('image_history_gallery', 'image_current_page', 'image_page_info'),
        show_progress=False
    )

    # Image selection from gallery
    shared.gradio['image_history_gallery'].select(
        on_gallery_select,
        gradio('image_current_page'),
        gradio('image_selected_path', 'image_settings_display'),
        show_progress=False
    )

    # Send to Generate
    shared.gradio['image_send_to_generate'].click(
        send_to_generate,
        gradio('image_selected_path'),
        gradio(
            'image_prompt',
            'image_neg_prompt',
            'image_width',
            'image_height',
            'image_aspect_ratio',
            'image_steps',
            'image_seed',
            'image_batch_size',
            'image_batch_count',
            'image_gallery_status'
        ),
        show_progress=False
    )


def generate(state):
    """
    Generate images using the loaded model.
    Automatically adjusts parameters based on pipeline type.
    """
    import torch
    import numpy as np

    model_name = state['image_model_menu']

    if not model_name or model_name == 'None':
        logger.error("No image model selected. Go to the Model tab and select a model.")
        return []

    if shared.image_model is None:
        result = load_image_model(
            model_name,
            dtype=state['image_dtype'],
            attn_backend=state['image_attn_backend'],
            cpu_offload=state['image_cpu_offload'],
            compile_model=state['image_compile'],
            quant_method=state['image_quant']
        )
        if result is None:
            logger.error(f"Failed to load model `{model_name}`.")
            return []

        shared.image_model_name = model_name

    seed = state['image_seed']
    if seed == -1:
        seed = np.random.randint(0, 2**32 - 1)

    generator = torch.Generator("cuda").manual_seed(int(seed))
    all_images = []

    # Get pipeline type for parameter adjustment
    pipeline_type = getattr(shared, 'image_pipeline_type', None)
    if pipeline_type is None:
        pipeline_type = get_pipeline_type(shared.image_model)

    # Build generation kwargs based on pipeline type
    gen_kwargs = {
        "prompt": state['image_prompt'],
        "negative_prompt": state['image_neg_prompt'],
        "height": int(state['image_height']),
        "width": int(state['image_width']),
        "num_inference_steps": int(state['image_steps']),
        "num_images_per_prompt": int(state['image_batch_size']),
        "generator": generator,
    }

    # Add pipeline-specific parameters
    if pipeline_type == 'qwenimage':
        # Qwen-Image uses true_cfg_scale instead of guidance_scale
        gen_kwargs["true_cfg_scale"] = state.get('image_cfg_scale', 4.0)
    else:
        # Z-Image and others use guidance_scale
        gen_kwargs["guidance_scale"] = state.get('image_cfg_scale', 0.0)

    t0 = time.time()
    for i in range(int(state['image_batch_count'])):
        generator.manual_seed(int(seed + i))
        batch_results = shared.image_model(**gen_kwargs).images
        all_images.extend(batch_results)

    t1 = time.time()
    save_generated_images(all_images, state, seed)

    logger.info(f'Images generated in {(t1-t0):.2f} seconds ({state["image_steps"]/(t1-t0):.2f} steps/s, seed {seed})')
    return all_images


def load_image_model_wrapper(model_name, dtype, attn_backend, cpu_offload, compile_model, quant_method):
    if not model_name or model_name == 'None':
        yield "No model selected"
        return

    try:
        yield f"Loading `{model_name}`..."
        unload_image_model()

        result = load_image_model(
            model_name,
            dtype=dtype,
            attn_backend=attn_backend,
            cpu_offload=cpu_offload,
            compile_model=compile_model,
            quant_method=quant_method
        )

        if result is not None:
            shared.image_model_name = model_name
            yield f"✓ Loaded **{model_name}** (quantization: {quant_method})"
        else:
            yield f"✗ Failed to load `{model_name}`"
    except Exception:
        yield f"Error:\n```\n{traceback.format_exc()}\n```"


def unload_image_model_wrapper():
    unload_image_model()
    if shared.image_model_name != 'None':
        return f"Model: **{shared.image_model_name}** (not loaded)"
    return "No model loaded"


def download_image_model_wrapper(model_path):
    from huggingface_hub import snapshot_download

    if not model_path:
        yield "No model specified", gr.update()
        return

    try:
        model_path = model_path.strip()
        if model_path.startswith('https://huggingface.co/'):
            model_path = model_path[len('https://huggingface.co/'):]
        elif model_path.startswith('huggingface.co/'):
            model_path = model_path[len('huggingface.co/'):]

        if ':' in model_path:
            model_id, branch = model_path.rsplit(':', 1)
        else:
            model_id, branch = model_path, 'main'

        folder_name = model_id.replace('/', '_')
        output_folder = Path(shared.args.image_model_dir) / folder_name

        yield f"Downloading `{model_id}` (branch: {branch})...", gr.update()

        snapshot_download(
            repo_id=model_id,
            revision=branch,
            local_dir=output_folder,
            local_dir_use_symlinks=False,
        )

        new_choices = utils.get_available_image_models()
        yield f"✓ Downloaded to `{output_folder}`", gr.update(choices=new_choices, value=folder_name)
    except Exception:
        yield f"Error:\n```\n{traceback.format_exc()}\n```", gr.update()