app.py

import os
import gradio as gr
import json
import logging
import torch
from PIL import Image
import spaces
from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler
from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard, snapshot_download
import copy
import random
import time
import re
import math
import numpy as np
import traceback

# Load LoRAs from JSON file
def load_loras_from_file():
    """Load LoRA configurations from external JSON file."""
    try:
        with open('loras.json', 'r', encoding='utf-8') as f:
            return json.load(f)
    except FileNotFoundError:
        print("Warning: loras.json file not found. Using empty list.")
        return []
    except json.JSONDecodeError as e:
        print(f"Error parsing loras.json: {e}")
        return []

# Load the LoRAs
loras = load_loras_from_file()

# Initialize the base model
dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"
base_model = "Qwen/Qwen-Image"

# Scheduler configuration from the Qwen-Image-Lightning repository
scheduler_config = {
    "base_image_seq_len": 256,
    "base_shift": math.log(3),
    "invert_sigmas": False,
    "max_image_seq_len": 8192,
    "max_shift": math.log(3),
    "num_train_timesteps": 1000,
    "shift": 1.0,
    "shift_terminal": None,
    "stochastic_sampling": False,
    "time_shift_type": "exponential",
    "use_beta_sigmas": False,
    "use_dynamic_shifting": True,
    "use_exponential_sigmas": False,
    "use_karras_sigmas": False,
}

scheduler = FlowMatchEulerDiscreteScheduler.from_config(scheduler_config)
pipe = DiffusionPipeline.from_pretrained(
    base_model, scheduler=scheduler, torch_dtype=dtype
).to(device)

# Lightning LoRA info (no global state)
LIGHTNING_LORA_REPO = "lightx2v/Qwen-Image-Lightning"
LIGHTNING_LORA_WEIGHT = "Qwen-Image-Lightning-4steps-V2.0-bf16.safetensors"
LIGHTNING8_LORA_WEIGHT = "Qwen-Image-Lightning-8steps-V2.0-bf16.safetensors"
LIGHTNING_FP8_4STEPS_LORA_WEIGHT = "Qwen-Image-fp8-e4m3fn-Lightning-4steps-V1.0-bf16.safetensors"


MAX_SEED = np.iinfo(np.int32).max

class calculateDuration:
    def __init__(self, activity_name=""):
        self.activity_name = activity_name

    def __enter__(self):
        self.start_time = time.time()
        return self
    
    def __exit__(self, exc_type, exc_value, traceback):
        self.end_time = time.time()
        self.elapsed_time = self.end_time - self.start_time
        if self.activity_name:
            print(f"Elapsed time for {self.activity_name}: {self.elapsed_time:.6f} seconds")
        else:
            print(f"Elapsed time: {self.elapsed_time:.6f} seconds")

def get_image_size(aspect_ratio):
    """Converts aspect ratio string to width, height tuple."""
    if aspect_ratio == "1:1":
        return 1024, 1024
    elif aspect_ratio == "16:9":
        return 1152, 640
    elif aspect_ratio == "9:16":
        return 640, 1152
    elif aspect_ratio == "4:3":
        return 1024, 768
    elif aspect_ratio == "3:4":
        return 768, 1024
    elif aspect_ratio == "3:2":
        return 1024, 688
    elif aspect_ratio == "2:3":
        return 688, 1024
    elif aspect_ratio == "4:1":
        return 2560, 640
    elif aspect_ratio == "3:1":
        return 1920, 640
    elif aspect_ratio == "2:1":
        return 1280, 640
    else:
        return 1024, 1024

def update_selection(evt: gr.SelectData, aspect_ratio):
    selected_lora = loras[evt.index]
    new_placeholder = f"Type a prompt for {selected_lora['title']}"
    lora_repo = selected_lora["repo"]
    updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✨"
    
    # Get model card examples
    examples_list = []
    try:
        model_card = ModelCard.load(lora_repo)
        widget_data = model_card.data.get("widget", [])
        if widget_data and len(widget_data) > 0:
            # Get examples from widget data
            for example in widget_data[:4]:
                if "output" in example and "url" in example["output"]:
                    image_url = f"https://huggingface.co/{lora_repo}/resolve/main/{example['output']['url']}"
                    prompt_text = example.get("text", "")
                    examples_list.append([prompt_text])
    except Exception as e:
        print(f"Could not load model card for {lora_repo}: {e}")
    
    # Update aspect ratio if specified in LoRA config
    # if "aspect" in selected_lora:
    #     if selected_lora["aspect"] == "portrait":
    #         aspect_ratio = "9:16"
    #     elif selected_lora["aspect"] == "landscape":
    #         aspect_ratio = "16:9"
    #     elif selected_lora["aspect"] == "square":
    #         aspect_ratio = "1:1"
    #     else:
    #         aspect_ratio = selected_lora["aspect"]
    
    return (
        gr.update(placeholder=new_placeholder),
        updated_text,
        evt.index,
        aspect_ratio,
        gr.update(interactive=True)
    )

def handle_speed_mode(speed_mode):
    """Update UI based on speed/quality toggle."""
    if speed_mode == "light 4":
        return gr.update(value="Light mode (4 steps) selected"), 4, 1.0
    elif speed_mode == "light 4 fp8":
        return gr.update(value="Light mode (4 steps fp8) selected"), 4, 1.0
    elif speed_mode == "light 8":
        return gr.update(value="Light mode (8 steps) selected"), 8, 1.0
    else: 
        return gr.update(value="Normal quality (45 steps) selected"), 45, 3.5

@spaces.GPU(duration=70)
def generate_image(
    prompt_mash,
    steps,
    seed,
    cfg_scale,
    width,
    height,
    lora_scale,
    negative_prompt="",
    num_images=1,
):
    pipe.to("cuda")

    # Seeds y generadores (seed, seed+100, ...)
    seeds = [seed + (i * 100) for i in range(num_images)]
    generators = [torch.Generator(device="cuda").manual_seed(s) for s in seeds]
    
    with calculateDuration("Generating image"):
        result = pipe(
            prompt=prompt_mash,
            negative_prompt=negative_prompt,
            num_inference_steps=steps,
            true_cfg_scale=cfg_scale,  # Qwen-Image
            width=width,
            height=height,
            num_images_per_prompt=num_images,  # 👈 una sola vez
            generator=generators,              #    lista de generators
        )

    # Devolver SIEMPRE lista de (imagen, seed)
    images = [(img, s) for img, s in zip(result.images, seeds)]
    return images

@spaces.GPU(duration=70)
def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, aspect_ratio, lora_scale, speed_mode, quality_multiplier, quantity, progress=gr.Progress(track_tqdm=True)):
    if selected_index is None:
        raise gr.Error("You must select a LoRA before proceeding.")
    
    selected_lora = loras[selected_index]
    lora_path = selected_lora["repo"]
    trigger_word = selected_lora["trigger_word"]
    
    # Prepare prompt with trigger word
    if trigger_word:
        if "trigger_position" in selected_lora:
            if selected_lora["trigger_position"] == "prepend":
                prompt_mash = f"{trigger_word} {prompt}"
            else:
                prompt_mash = f"{prompt} {trigger_word}"
        else:
            prompt_mash = f"{trigger_word} {prompt}"
    else:
        prompt_mash = prompt

    # Always unload any existing LoRAs first to avoid conflicts
    with calculateDuration("Unloading existing LoRAs"):
        pipe.unload_lora_weights()

    # Load LoRAs based on speed mode
    if speed_mode == "light 4":
        with calculateDuration("Loading Lightning LoRA and style LoRA"):
            # Load Lightning LoRA first
            pipe.load_lora_weights(
                LIGHTNING_LORA_REPO, 
                weight_name=LIGHTNING_LORA_WEIGHT,
                adapter_name="lightning"
            )
            
            # Load the selected style LoRA
            weight_name = selected_lora.get("weights", None)
            pipe.load_lora_weights(
                lora_path, 
                weight_name=weight_name, 
                low_cpu_mem_usage=True,
                adapter_name="style"
            )
            
            # Set both adapters active with their weights
            pipe.set_adapters(["lightning", "style"], adapter_weights=[1.0, lora_scale])
    elif speed_mode == "light 4 fp8":
        with calculateDuration("Loading Lightning LoRA and style LoRA"):
            # Load Lightning LoRA first
            pipe.load_lora_weights(
                LIGHTNING_LORA_REPO, 
                weight_name=LIGHTNING_FP8_4STEPS_LORA_WEIGHT,
                adapter_name="lightning"
            )
            
            # Load the selected style LoRA
            weight_name = selected_lora.get("weights", None)
            pipe.load_lora_weights(
                lora_path, 
                weight_name=weight_name, 
                low_cpu_mem_usage=True,
                adapter_name="style"
            )
            
            # Set both adapters active with their weights
            pipe.set_adapters(["lightning", "style"], adapter_weights=[1.0, lora_scale])
    elif speed_mode == "light 8":
        with calculateDuration("Loading Lightning LoRA and style LoRA"):
            # Load Lightning LoRA first
            pipe.load_lora_weights(
                LIGHTNING_LORA_REPO, 
                weight_name=LIGHTNING8_LORA_WEIGHT,
                adapter_name="lightning"
            )
            
            # Load the selected style LoRA
            weight_name = selected_lora.get("weights", None)
            pipe.load_lora_weights(
                lora_path, 
                weight_name=weight_name, 
                low_cpu_mem_usage=True,
                adapter_name="style"
            )
            
            # Set both adapters active with their weights
            pipe.set_adapters(["lightning", "style"], adapter_weights=[1.0, lora_scale])
    else:
        # Quality mode - only load the style LoRA
        with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"):
            weight_name = selected_lora.get("weights", None)
            pipe.load_lora_weights(
                lora_path, 
                weight_name=weight_name, 
                low_cpu_mem_usage=True,
                adapter_name="style"
            )
            pipe.set_adapters(["style"], adapter_weights=[lora_scale])
                
    # Set random seed for reproducibility
    with calculateDuration("Randomizing seed"):
        if randomize_seed:
            seed = random.randint(0, MAX_SEED)
    
    # Get image dimensions from aspect ratio
    width, height = get_image_size(aspect_ratio)

    # Apply quality multiplier
    multiplier = float(quality_multiplier.replace('x', ''))
    width = int(width * multiplier)
    height = int(height * multiplier)
    
    # quantity es índice (0..3) -> cantidad (1..4)
    num_images = int(quantity) + 1

    # Generar
    pairs = generate_image(
        prompt_mash,
        steps,
        seed,
        cfg_scale,
        width,
        height,
        lora_scale,
        negative_prompt="",     # ajustá si usás uno real
        num_images=num_images,
    )

    # Formatear para Gallery: (img, "Seed: N")
    #images_for_gallery = [(img, f"Seed: {s}") for (img, s) in pairs]
    # images_for_gallery = [
    #     (
    #         img,
    #         s
    #     )
    #     for (img, s) in pairs
    # ]
    images_for_gallery = [
        (img, str(s))
        for (img, s) in pairs
    ]

    # Debe devolver DOS valores porque outputs=[result, seed]
    return images_for_gallery, seed

def get_huggingface_safetensors(link):
    split_link = link.split("/")
    if len(split_link) != 2:
        raise Exception("Invalid Hugging Face repository link format.")

    print(f"Repository attempted: {split_link}")
    
    # Load model card
    model_card = ModelCard.load(link)
    base_model = model_card.data.get("base_model")
    print(f"Base model: {base_model}")

    # Validate model type (for Qwen-Image)
    acceptable_models = {"Qwen/Qwen-Image"}
    
    models_to_check = base_model if isinstance(base_model, list) else [base_model]
    
    if not any(model in acceptable_models for model in models_to_check):
        raise Exception("Not a Qwen-Image LoRA!")
        
    # Extract image and trigger word
    image_path = model_card.data.get("widget", [{}])[0].get("output", {}).get("url", None)
    trigger_word = model_card.data.get("instance_prompt", "")
    image_url = f"https://huggingface.co/{link}/resolve/main/{image_path}" if image_path else None

    # Initialize Hugging Face file system
    fs = HfFileSystem()
    try:
        list_of_files = fs.ls(link, detail=False)
        
        # Find safetensors file
        safetensors_name = None
        for file in list_of_files:
            filename = file.split("/")[-1]
            if filename.endswith(".safetensors"):
                safetensors_name = filename
                break

        if not safetensors_name:
            raise Exception("No valid *.safetensors file found in the repository.")

    except Exception as e:
        print(e)
        raise Exception("You didn't include a valid Hugging Face repository with a *.safetensors LoRA")
    
    return split_link[1], link, safetensors_name, trigger_word, image_url

def check_custom_model(link):
    print(f"Checking a custom model on: {link}")
    
    if link.endswith('.safetensors'):
        if 'huggingface.co' in link:
            parts = link.split('/')
            try:
                hf_index = parts.index('huggingface.co')
                username = parts[hf_index + 1]
                repo_name = parts[hf_index + 2]
                repo = f"{username}/{repo_name}"
                
                safetensors_name = parts[-1]
                
                try:
                    model_card = ModelCard.load(repo)
                    trigger_word = model_card.data.get("instance_prompt", "")
                    image_path = model_card.data.get("widget", [{}])[0].get("output", {}).get("url", None)
                    image_url = f"https://huggingface.co/{repo}/resolve/main/{image_path}" if image_path else None
                except:
                    trigger_word = ""
                    image_url = None
                
                return repo_name, repo, safetensors_name, trigger_word, image_url
            except:
                raise Exception("Invalid safetensors URL format")
    
    if link.startswith("https://"):
        if link.startswith("https://huggingface.co") or link.startswith("https://www.huggingface.co"):
            link_split = link.split("huggingface.co/")
            return get_huggingface_safetensors(link_split[1])
    else: 
        return get_huggingface_safetensors(link)

def add_custom_lora(custom_lora):
    global loras
    if custom_lora:
        try:
            title, repo, path, trigger_word, image = check_custom_model(custom_lora)
            print(f"Loaded custom LoRA: {repo}")
            
            # Get model card examples for custom LoRA
            model_card_examples = ""
            try:
                model_card = ModelCard.load(repo)
                widget_data = model_card.data.get("widget", [])
                if widget_data and len(widget_data) > 0:
                    examples_html = '<div style="margin-top: 10px;">'
                    examples_html += '<h4 style="margin-bottom: 8px; font-size: 0.9em;">Sample Images:</h4>'
                    examples_html += '<div style="display: grid; grid-template-columns: repeat(4, 1fr); gap: 8px;">'
                    
                    for i, example in enumerate(widget_data[:4]):
                        if "output" in example and "url" in example["output"]:
                            image_url = f"https://huggingface.co/{repo}/resolve/main/{example['output']['url']}"
                            caption = example.get("text", f"Example {i+1}")
                            examples_html += f'''
                            <div style="text-align: center;">
                                <img src="{image_url}" style="width: 100%; height: auto; border-radius: 4px;" />
                                <p style="font-size: 0.7em; margin: 2px 0;">{caption[:30]}{'...' if len(caption) > 30 else ''}</p>
                            </div>
                            '''
                    
                    examples_html += '</div></div>'
                    model_card_examples = examples_html
            except Exception as e:
                print(f"Could not load model card examples for custom LoRA: {e}")
            
            card = f'''
            <div class="custom_lora_card">
              <span>Loaded custom LoRA:</span>
              <div class="card_internal">
                <img src="{image}" />
                <div>
                    <h3>{title}</h3>
                    <small>{"Using: <code><b>"+trigger_word+"</code></b> as the trigger word" if trigger_word else "No trigger word found. If there's a trigger word, include it in your prompt"}<br></small>
                </div>
              </div>
              {model_card_examples}
            </div>
            '''
            existing_item_index = next((index for (index, item) in enumerate(loras) if item['repo'] == repo), None)
            if existing_item_index is None:
                new_item = {
                    "image": image,
                    "title": title,
                    "repo": repo,
                    "weights": path,
                    "trigger_word": trigger_word
                }
                print(new_item)
                loras.append(new_item)
                existing_item_index = len(loras) - 1  # Get the actual index after adding
                
            return gr.update(visible=True, value=card), gr.update(visible=True), gr.Gallery(selected_index=None), f"Custom: {path}", existing_item_index, trigger_word, gr.update(interactive=True)
        except Exception as e:
            full_traceback = traceback.format_exc()
            print(f"Full traceback:\n{full_traceback}")
            gr.Warning(f"Invalid LoRA: either you entered an invalid link, or a non-Qwen-Image LoRA, this was the issue: {e}")
            return gr.update(visible=True, value=f"Invalid LoRA: either you entered an invalid link, a non-Qwen-Image LoRA"), gr.update(visible=True), gr.update(), "", None, "", gr.update(interactive=False)
    else:
        return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, "", gr.update(interactive=False)

def remove_custom_lora():
    return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, "", gr.update(interactive=False)

run_lora.zerogpu = True

css = '''
#gen_btn{height: 100%}
#gen_column{align-self: stretch}
#title{text-align: center}
#title h1{font-size: 3em; display:inline-flex; align-items:center}
#title img{width: 100px; margin-right: 0.5em}
#gallery .grid-wrap{height: 10vh}
#lora_list{background: var(--block-background-fill);padding: 0 1em .3em; font-size: 90%}
.card_internal{display: flex;height: 100px;margin-top: .5em}
.card_internal img{margin-right: 1em}
.styler{--form-gap-width: 0px !important}
#speed_status{padding: .5em; border-radius: 5px; margin: 1em 0}
'''

with gr.Blocks(theme=gr.themes.Soft(), css=css, delete_cache=(60, 60)) as app:
    title = gr.HTML(
        """<img src=\"https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/qwen_image_logo.png\" alt=\"Qwen-Image\" style=\"width: 280px; margin: 0 auto\">        
        <h3 style=\"margin-top: -10px\">LoRA🦜 ChoquinLabs Explorer</h3>""",
        elem_id="title",
    )
    
    selected_index = gr.State(None)
    
    with gr.Row():
        with gr.Column(scale=3):
            prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Type a prompt after selecting a LoRA")
        with gr.Column(scale=1, elem_id="gen_column"):
            generate_button = gr.Button("Generate", variant="primary", elem_id="gen_btn", interactive=False)
    
    with gr.Row():
        with gr.Column():
            selected_info = gr.Markdown("")
            examples_component = gr.Examples(examples=[], inputs=[prompt], label="Sample Prompts", visible=False)
            gallery = gr.Gallery(
                [(item["image"], item["title"]) for item in loras],
                label="LoRA Gallery",
                allow_preview=False,
                columns=3,
                elem_id="gallery",
                show_share_button=False
            )
            with gr.Group():
                custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path", placeholder="username/qwen-image-custom-lora")
                gr.Markdown("[Check Qwen-Image LoRAs](https://huggingface.co/models?other=base_model:adapter:Qwen/Qwen-Image)", elem_id="lora_list")
            custom_lora_info = gr.HTML(visible=False)
            custom_lora_button = gr.Button("Remove custom LoRA", visible=False)
        
        with gr.Column():
            result = gr.Gallery(label="Generated Images", show_label=True, elem_id="result_gallery")
            
            with gr.Row():
                with gr.Column():
                    speed_mode = gr.Radio(
                        label="Generation Mode",
                        choices=["light 4", "light 4 fp8", "light 8", "normal"],
                        value="light 4",
                        info="'light' modes use Lightning LoRA for faster generation"
                    )
                with gr.Column():
                    quantity = gr.Radio(
                        label="Quantity",
                        choices=["1", "2", "3", "4"],
                        value="1",
                        type="index"
                    )
            
            speed_status = gr.Markdown("Quality mode active", elem_id="speed_status")

            with gr.Row():
                aspect_ratio = gr.Radio(
                    label="Aspect Ratio",
                    choices=["1:1", "16:9", "9:16", "4:3", "3:4", "3:2", "2:3", "4:1", "3:1", "2:1"],
                    value="16:9"
                )

            with gr.Row():
                quality_multiplier = gr.Radio(
                    label="Quality (Size Multiplier)",
                    choices=["0.5x", "1x", "1.5x"],
                    value="1x"
                )

    with gr.Row():
        with gr.Accordion("Advanced Settings", open=False):
            with gr.Column():
                with gr.Row():
                    cfg_scale = gr.Slider(
                        label="Guidance Scale (True CFG)", 
                        minimum=1.0, 
                        maximum=5.0, 
                        step=0.1, 
                        value=3.5,
                        info="Lower for speed mode, higher for quality"
                    )
                    steps = gr.Slider(
                        label="Steps", 
                        minimum=4, 
                        maximum=50, 
                        step=1, 
                        value=45,
                        info="Automatically set by speed mode"
                    )
                
                with gr.Row():
                    randomize_seed = gr.Checkbox(True, label="Randomize seed")
                    seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True)
                    lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=3, step=0.01, value=1.0)

    # Event handlers
    gallery.select(
        update_selection,
        inputs=[aspect_ratio],
        outputs=[prompt, selected_info, selected_index, aspect_ratio, generate_button]
    )
    
    speed_mode.change(
        handle_speed_mode,
        inputs=[speed_mode],
        outputs=[speed_status, steps, cfg_scale]
    )
    
    custom_lora.input(
        add_custom_lora,
        inputs=[custom_lora],
        outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, prompt, generate_button]
    )
    
    custom_lora_button.click(
        remove_custom_lora,
        outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, custom_lora, generate_button]
    )
    
    gr.on(
        triggers=[generate_button.click, prompt.submit],
        fn=run_lora,
        inputs=[prompt, cfg_scale, steps, selected_index, randomize_seed, seed, aspect_ratio, lora_scale, speed_mode, quality_multiplier, quantity],
        outputs=[result, seed]
    )
 
    app.load(
        fn=handle_speed_mode,
        inputs=[gr.State("light 4")],
        outputs=[speed_status, steps, cfg_scale]
    )

app.queue()
app.launch()