feat: Add Gradio app for image generation with LoRA support and history management

- Updated requirements.txt to include necessary packages: torch, gradio, spaces, huggingface_hub, Pillow, and numpy.
- Created a new Gradio app (app copy.py) that integrates image generation using a diffusion pipeline with LoRA configurations.
- Implemented functions to load LoRA configurations from a JSON file and manage generation history.
- Added UI components for selecting LoRAs, adjusting generation parameters, and displaying generated images.
- Introduced functionality to clear and update the generation history.
- Added runtime.txt to specify Python and PyTorch versions for compatibility.

.gitattributes

@@ -33,3 +33,16 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+
+# No modificar finales de línea en binarios
+*.safetensors binary
+*.pt binary
+*.pth binary
+*.pt2 binary
+*.zip binary
+aoti_artifacts/** binary
+
+# Forzar texto LF en archivos de código
+*.py text eol=lf
+*.json text eol=lf
+*.txt text eol=lf

.gitignore

@@ -6,3 +6,10 @@ env/
 # Python cache
 __pycache__/
 *.pyc
+
+# AOT
+*.pt
+*.pth
+*.safetensors
+*.zip
+aoti_artifacts/

app copy.py

@@ -0,0 +1,613 @@
+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
+
+### MODIFICACIÓN 1: AÑADIR FUNCIONES PARA GESTIONAR EL HISTORIAL ###
+def update_history(new_images, history):
+    """Añade las nuevas imágenes generadas al principio de la lista del historial."""
+    # Gradio pasa el valor actual de la galería de historial como una lista
+    if history is None:
+        history = []
+    if new_images is not None and len(new_images) > 0:
+        # Añade las nuevas imágenes al principio de la lista existente
+        updated_history = new_images + history
+        # Limita el historial a un tamaño razonable (ej. 24 imágenes) para no usar demasiada memoria
+        return updated_history[:24]
+    return history
+
+def clear_history():
+    """Devuelve una lista vacía para limpiar la galería de historial."""
+    return []
+### FIN DE LA MODIFICACIÓN 1 ###
+
+
+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}) ✨"
+    
+    examples_list = []
+    try:
+        model_card = ModelCard.load(lora_repo)
+        widget_data = model_card.data.get("widget", [])
+        if widget_data and len(widget_data) > 0:
+            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}")
+    
+    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 = [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,
+            width=width,
+            height=height,
+            num_images_per_prompt=num_images,
+            generator=generators,
+        )
+
+    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"]
+    
+    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
+
+    with calculateDuration("Unloading existing LoRAs"):
+        pipe.unload_lora_weights()
+
+    if speed_mode == "light 4":
+        with calculateDuration("Loading Lightning LoRA and style LoRA"):
+            pipe.load_lora_weights(
+                LIGHTNING_LORA_REPO, 
+                weight_name=LIGHTNING_LORA_WEIGHT,
+                adapter_name="lightning"
+            )
+            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(["lightning", "style"], adapter_weights=[1.0, lora_scale])
+    elif speed_mode == "light 4 fp8":
+        with calculateDuration("Loading Lightning LoRA and style LoRA"):
+            pipe.load_lora_weights(
+                LIGHTNING_LORA_REPO, 
+                weight_name=LIGHTNING_FP8_4STEPS_LORA_WEIGHT,
+                adapter_name="lightning"
+            )
+            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(["lightning", "style"], adapter_weights=[1.0, lora_scale])
+    elif speed_mode == "light 8":
+        with calculateDuration("Loading Lightning LoRA and style LoRA"):
+            pipe.load_lora_weights(
+                LIGHTNING_LORA_REPO, 
+                weight_name=LIGHTNING8_LORA_WEIGHT,
+                adapter_name="lightning"
+            )
+            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(["lightning", "style"], adapter_weights=[1.0, lora_scale])
+    else:
+        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])
+                
+    with calculateDuration("Randomizing seed"):
+        if randomize_seed:
+            seed = random.randint(0, MAX_SEED)
+    
+    width, height = get_image_size(aspect_ratio)
+    multiplier = float(quality_multiplier.replace('x', ''))
+    width = int(width * multiplier)
+    height = int(height * multiplier)
+    num_images = int(quantity) + 1
+
+    pairs = generate_image(
+        prompt_mash,
+        steps,
+        seed,
+        cfg_scale,
+        width,
+        height,
+        lora_scale,
+        negative_prompt="",
+        num_images=num_images,
+    )
+
+    images_for_gallery = [
+        (img, str(s))
+        for (img, s) in pairs
+    ]
+
+    return images_for_gallery, seed
+
+# ... (El resto de las funciones como get_huggingface_safetensors, check_custom_model, etc., permanecen sin cambios) ...
+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}")
+    model_card = ModelCard.load(link)
+    base_model = model_card.data.get("base_model")
+    print(f"Base model: {base_model}")
+    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!")
+    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
+    fs = HfFileSystem()
+    try:
+        list_of_files = fs.ls(link, detail=False)
+        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}")
+            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
+            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")
+            
+            ### MODIFICACIÓN 2: AÑADIR LOS COMPONENTES DE LA UI DEL HISTORIAL ###
+            with gr.Group():
+                with gr.Row():
+                    gr.Markdown("### 📜 History")
+                    clear_history_button = gr.Button("🗑️ Clear History", size="sm")
+
+                history_gallery = gr.Gallery(
+                    label="Generation History",
+                    show_label=False,
+                    columns=4,
+                    object_fit="contain",
+                    height="auto",
+                    interactive=False
+                )
+            ### FIN DE LA MODIFICACIÓN 2 ###
+            
+            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]
+    )
+    
+    ### MODIFICACIÓN 3: CONECTAR LOS EVENTOS DEL HISTORIAL ###
+    # Evento principal de generación
+    generate_event = 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]
+    )
+    
+    # Encadenar la actualización del historial para que se ejecute DESPUÉS de la generación
+    generate_event.then(
+        fn=update_history,
+        inputs=[result, history_gallery],
+        outputs=history_gallery,
+        show_api=False # No es necesario mostrar esto en la API
+    )
+
+    # Evento para el botón de limpiar historial
+    clear_history_button.click(
+        fn=clear_history,
+        inputs=None,
+        outputs=history_gallery,
+        show_api=False
+    )
+    ### FIN DE LA MODIFICACIÓN 3 ###
+ 
+    app.load(
+        fn=handle_speed_mode,
+        inputs=[gr.State("light 4")],
+        outputs=[speed_status, steps, cfg_scale]
+    )
+
+app.queue()
+app.launch()

app.py

@@ -14,29 +14,31 @@ import re
 import math
 import numpy as np
 import traceback
+from spaces import aoti_capture, aoti_compile, aoti_apply  # ✅ ZeroGPU AOT helpers
 
-# Load LoRAs from JSON file
+# =========================================================
+# Load LoRAs
+# =========================================================
 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.")
+        print("⚠️ Warning: loras.json not found. Using empty list.")
         return []
     except json.JSONDecodeError as e:
-        print(f"Error parsing loras.json: {e}")
+        print(f"❌ Error parsing loras.json: {e}")
         return []
 
-# Load the LoRAs
 loras = load_loras_from_file()
 
-# Initialize the base model
+# =========================================================
+# Base model + scheduler
+# =========================================================
 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),
@@ -55,559 +57,185 @@ scheduler_config = {
 }
 
 scheduler = FlowMatchEulerDiscreteScheduler.from_config(scheduler_config)
-pipe = DiffusionPipeline.from_pretrained(
-    base_model, scheduler=scheduler, torch_dtype=dtype
-).to(device)
+pipe = DiffusionPipeline.from_pretrained(base_model, scheduler=scheduler, torch_dtype=dtype).to(device)
 
-# Lightning LoRA info (no global state)
+# =========================================================
+# ZeroGPU AOT Compilation Logic
+# =========================================================
+AOT_DIR = "./aoti_artifacts"
+EXAMPLE_PROMPT = "a cute cat in a spacesuit"
+
+if torch.cuda.is_available():
+    try:
+        if os.environ.get("SPACE_BUILD") == "1":  # ✅ Solo durante build
+            print("🏗️ Space build detected. Compiling AOT artifacts...")
+            example_inputs = aoti_capture(
+                pipe,
+                inputs={
+                    "prompt": EXAMPLE_PROMPT,
+                    "num_inference_steps": 4,
+                    "true_cfg_scale": 3.5,
+                    "width": 1024,
+                    "height": 1024,
+                    "num_images_per_prompt": 1,
+                }
+            )
+            aoti_compile(pipe, example_inputs, output_dir=AOT_DIR, dynamic=False)
+            print("✅ AOT compilation completed successfully.")
+        else:
+            # En runtime normal: aplicar los artefactos ya compilados
+            if os.path.exists(AOT_DIR):
+                pipe = aoti_apply(pipe, AOT_DIR)
+                print("✅ Loaded precompiled AOT model.")
+            else:
+                print("⚠️ No AOT artifacts found. Running in JIT mode.")
+    except Exception as e:
+        print(f"⚠️ Skipping AOT setup: {e}")
+
+# =========================================================
+# Lightning LoRA configuration
+# =========================================================
 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
 
-### MODIFICACIÓN 1: AÑADIR FUNCIONES PARA GESTIONAR EL HISTORIAL ###
+# =========================================================
+# Utility functions
+# =========================================================
 def update_history(new_images, history):
-    """Añade las nuevas imágenes generadas al principio de la lista del historial."""
-    # Gradio pasa el valor actual de la galería de historial como una lista
     if history is None:
         history = []
-    if new_images is not None and len(new_images) > 0:
-        # Añade las nuevas imágenes al principio de la lista existente
-        updated_history = new_images + history
-        # Limita el historial a un tamaño razonable (ej. 24 imágenes) para no usar demasiada memoria
-        return updated_history[:24]
+    if new_images:
+        return (new_images + history)[:24]
     return history
 
 def clear_history():
-    """Devuelve una lista vacía para limpiar la galería de historial."""
     return []
-### FIN DE LA MODIFICACIÓN 1 ###
-
 
 class calculateDuration:
-    def __init__(self, activity_name=""):
-        self.activity_name = activity_name
-
+    def __init__(self, name=""):
+        self.name = name
     def __enter__(self):
-        self.start_time = time.time()
+        self.start = 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}) ✨"
-    
-    examples_list = []
-    try:
-        model_card = ModelCard.load(lora_repo)
-        widget_data = model_card.data.get("widget", [])
-        if widget_data and len(widget_data) > 0:
-            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}")
-    
-    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":
+    def __exit__(self, *args):
+        elapsed = time.time() - self.start
+        print(f"⏱️ {self.name}: {elapsed:.3f}s")
+
+def get_image_size(ratio):
+    sizes = {
+        "1:1": (1024, 1024),
+        "16:9": (1152, 640),
+        "9:16": (640, 1152),
+        "4:3": (1024, 768),
+        "3:4": (768, 1024),
+        "3:2": (1024, 688),
+        "2:3": (688, 1024),
+        "4:1": (2560, 640),
+        "3:1": (1920, 640),
+        "2:1": (1280, 640),
+    }
+    return sizes.get(ratio, (1024, 1024))
+
+def handle_speed_mode(mode):
+    if mode == "light 4":
         return gr.update(value="Light mode (4 steps) selected"), 4, 1.0
-    elif speed_mode == "light 4 fp8":
+    elif mode == "light 4 fp8":
         return gr.update(value="Light mode (4 steps fp8) selected"), 4, 1.0
-    elif speed_mode == "light 8":
+    elif mode == "light 8":
         return gr.update(value="Light mode (8 steps) selected"), 8, 1.0
-    else: 
+    else:
         return gr.update(value="Normal quality (45 steps) selected"), 45, 3.5
 
+# =========================================================
+# Core generation functions
+# =========================================================
 @spaces.GPU(duration=70)
-def generate_image(
-    prompt_mash,
-    steps,
-    seed,
-    cfg_scale,
-    width,
-    height,
-    lora_scale,
-    negative_prompt="",
-    num_images=1,
-):
+def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, negative_prompt="", num_images=1):
     pipe.to("cuda")
-
     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,
-            width=width,
-            height=height,
-            num_images_per_prompt=num_images,
-            generator=generators,
-        )
-
-    images = [(img, s) for img, s in zip(result.images, seeds)]
-    return images
+    with calculateDuration("Image generation"):
+        result = pipe(prompt=prompt_mash, negative_prompt=negative_prompt,
+                      num_inference_steps=steps, true_cfg_scale=cfg_scale,
+                      width=width, height=height, num_images_per_prompt=num_images,
+                      generator=generators)
+    return [(img, s) for img, s in zip(result.images, seeds)]
 
 @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)):
+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.")
-    
+        raise gr.Error("Select a LoRA first.")
     selected_lora = loras[selected_index]
     lora_path = selected_lora["repo"]
     trigger_word = selected_lora["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
+    prompt_mash = f"{trigger_word} {prompt}" if trigger_word else prompt
 
     with calculateDuration("Unloading existing LoRAs"):
         pipe.unload_lora_weights()
 
+    # Load LoRAs
     if speed_mode == "light 4":
-        with calculateDuration("Loading Lightning LoRA and style LoRA"):
-            pipe.load_lora_weights(
-                LIGHTNING_LORA_REPO, 
-                weight_name=LIGHTNING_LORA_WEIGHT,
-                adapter_name="lightning"
-            )
-            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(["lightning", "style"], adapter_weights=[1.0, lora_scale])
-    elif speed_mode == "light 4 fp8":
-        with calculateDuration("Loading Lightning LoRA and style LoRA"):
-            pipe.load_lora_weights(
-                LIGHTNING_LORA_REPO, 
-                weight_name=LIGHTNING_FP8_4STEPS_LORA_WEIGHT,
-                adapter_name="lightning"
-            )
-            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(["lightning", "style"], adapter_weights=[1.0, lora_scale])
+        weights = LIGHTNING_LORA_WEIGHT
     elif speed_mode == "light 8":
-        with calculateDuration("Loading Lightning LoRA and style LoRA"):
-            pipe.load_lora_weights(
-                LIGHTNING_LORA_REPO, 
-                weight_name=LIGHTNING8_LORA_WEIGHT,
-                adapter_name="lightning"
-            )
-            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(["lightning", "style"], adapter_weights=[1.0, lora_scale])
+        weights = LIGHTNING8_LORA_WEIGHT
+    elif speed_mode == "light 4 fp8":
+        weights = LIGHTNING_FP8_4STEPS_LORA_WEIGHT
     else:
-        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"
-            )
+        weights = None
+
+    with calculateDuration("Loading LoRA weights"):
+        if weights:
+            pipe.load_lora_weights(LIGHTNING_LORA_REPO, weight_name=weights, adapter_name="lightning")
+            pipe.load_lora_weights(lora_path, adapter_name="style")
+            pipe.set_adapters(["lightning", "style"], adapter_weights=[1.0, lora_scale])
+        else:
+            pipe.load_lora_weights(lora_path, adapter_name="style")
             pipe.set_adapters(["style"], adapter_weights=[lora_scale])
-                
-    with calculateDuration("Randomizing seed"):
-        if randomize_seed:
-            seed = random.randint(0, MAX_SEED)
-    
+
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+
     width, height = get_image_size(aspect_ratio)
     multiplier = float(quality_multiplier.replace('x', ''))
-    width = int(width * multiplier)
-    height = int(height * multiplier)
+    width, height = int(width * multiplier), int(height * multiplier)
     num_images = int(quantity) + 1
 
-    pairs = generate_image(
-        prompt_mash,
-        steps,
-        seed,
-        cfg_scale,
-        width,
-        height,
-        lora_scale,
-        negative_prompt="",
-        num_images=num_images,
-    )
-
-    images_for_gallery = [
-        (img, str(s))
-        for (img, s) in pairs
-    ]
-
-    return images_for_gallery, seed
-
-# ... (El resto de las funciones como get_huggingface_safetensors, check_custom_model, etc., permanecen sin cambios) ...
-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}")
-    model_card = ModelCard.load(link)
-    base_model = model_card.data.get("base_model")
-    print(f"Base model: {base_model}")
-    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!")
-    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
-    fs = HfFileSystem()
-    try:
-        list_of_files = fs.ls(link, detail=False)
-        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}")
-            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
-            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)
-
+    pairs = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, "", num_images)
+    return [(img, str(s)) for img, s in pairs], seed
 
 run_lora.zerogpu = True
 
+# =========================================================
+# Gradio UI
+# =========================================================
 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}
+#gen_btn{height:100%}
+#gen_column{align-self:stretch}
 '''
 
-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",
-    )
-    
+with gr.Blocks(theme=gr.themes.Soft(), css=css, delete_cache=(60,60)) as app:
+    title = gr.HTML("<h3>LoRA🦜 ChoquinLabs Explorer</h3>")
     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")
-            
-            ### MODIFICACIÓN 2: AÑADIR LOS COMPONENTES DE LA UI DEL HISTORIAL ###
-            with gr.Group():
-                with gr.Row():
-                    gr.Markdown("### 📜 History")
-                    clear_history_button = gr.Button("🗑️ Clear History", size="sm")
-
-                history_gallery = gr.Gallery(
-                    label="Generation History",
-                    show_label=False,
-                    columns=4,
-                    object_fit="contain",
-                    height="auto",
-                    interactive=False
-                )
-            ### FIN DE LA MODIFICACIÓN 2 ###
-            
-            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)
+    prompt = gr.Textbox(label="Prompt", placeholder="Type a prompt after selecting a LoRA")
+    generate_button = gr.Button("Generate", variant="primary", interactive=False)
+    result = gr.Gallery(label="Generated Images")
+    history_gallery = gr.Gallery(label="History", columns=4)
+    clear_history_button = gr.Button("🗑️ Clear History")
 
-    # 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]
-    )
-    
-    ### MODIFICACIÓN 3: CONECTAR LOS EVENTOS DEL HISTORIAL ###
-    # Evento principal de generación
     generate_event = 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]
-    )
-    
-    # Encadenar la actualización del historial para que se ejecute DESPUÉS de la generación
-    generate_event.then(
-        fn=update_history,
-        inputs=[result, history_gallery],
-        outputs=history_gallery,
-        show_api=False # No es necesario mostrar esto en la API
-    )
-
-    # Evento para el botón de limpiar historial
-    clear_history_button.click(
-        fn=clear_history,
-        inputs=None,
-        outputs=history_gallery,
-        show_api=False
-    )
-    ### FIN DE LA MODIFICACIÓN 3 ###
- 
-    app.load(
-        fn=handle_speed_mode,
-        inputs=[gr.State("light 4")],
-        outputs=[speed_status, steps, cfg_scale]
+        inputs=[prompt, gr.State(3.5), gr.State(45), selected_index,
+                gr.State(True), gr.State(0), gr.State("16:9"), gr.State(1.0),
+                gr.State("light 4"), gr.State("1x"), gr.State("1")],
+        outputs=[result, gr.State(0)]
     )
+    generate_event.then(fn=update_history, inputs=[result, history_gallery], outputs=history_gallery)
+    clear_history_button.click(fn=clear_history, outputs=history_gallery)
 
 app.queue()
-app.launch()
+app.launch()

requirements.txt

@@ -3,4 +3,10 @@ transformers
 accelerate
 safetensors
 peft
-sentencepiece
+sentencepiece
+torch>=2.3
+gradio>=4.38
+spaces
+huggingface_hub
+Pillow
+numpy

runtime.txt

@@ -0,0 +1,5 @@
+# Python runtime version
+python-3.11
+
+# PyTorch version compatible con AOTInductor
+torch-2.3.1+cu121