Update app.py

app.py

@@ -27,52 +27,64 @@ import shutil
 import uuid
 import zipfile
 
-loras = [
-    # Sample Qwen-compatible LoRAs
+# META: CUDA_CHECK / GPU_INFO
+print("CUDA_VISIBLE_DEVICES=", os.environ.get("CUDA_VISIBLE_DEVICES"))
+print("torch.__version__ =", torch.__version__)
+print("torch.version.cuda =", torch.version.cuda)
+print("cuda available:", torch.cuda.is_available())
+print("cuda device count:", torch.cuda.device_count())
+if torch.cuda.is_available():
+    print("current device:", torch.cuda.current_device())
+    print("device name:", torch.cuda.get_device_name(torch.cuda.current_device()))
+
+print("Using device:", device)
+
+# List of predefined style models (formerly LoRAs)
+style_definitions = [
     {
-        "image": "https://huggingface.co/prithivMLmods/Qwen-Image-Studio-Realism/resolve/main/images/2.png",
-        "title": "Studio Realism",
-        "repo": "prithivMLmods/Qwen-Image-Studio-Realism",
-        "weights": "qwen-studio-realism.safetensors",
-        "trigger_word": "Studio Realism"
+        "thumbnail_url": "https://huggingface.co/prithivMLmods/Qwen-Image-Studio-Realism/resolve/main/images/2.png",
+        "style_name": "Studio Realism",
+        "repo_id": "prithivMLmods/Qwen-Image-Studio-Realism",
+        "weight_file": "qwen-studio-realism.safetensors",
+        "activation_phrase": "Studio Realism"
     },
     {
-        "image": "https://huggingface.co/prithivMLmods/Qwen-Image-Sketch-Smudge/resolve/main/images/1.png",
-        "title": "Sketch Smudge",
-        "repo": "prithivMLmods/Qwen-Image-Sketch-Smudge",
-        "weights": "qwen-sketch-smudge.safetensors",
-        "trigger_word": "Sketch Smudge"
+        "thumbnail_url": "https://huggingface.co/prithivMLmods/Qwen-Image-Sketch-Smudge/resolve/main/images/1.png",
+        "style_name": "Sketch Smudge",
+        "repo_id": "prithivMLmods/Qwen-Image-Sketch-Smudge",
+        "weight_file": "qwen-sketch-smudge.safetensors",
+        "activation_phrase": "Sketch Smudge"
     },
     {
-        "image": "https://huggingface.co/prithivMLmods/Qwen-Image-Anime-LoRA/resolve/main/images/1.png",
-        "title": "Qwen Anime",
-        "repo": "prithivMLmods/Qwen-Image-Anime-LoRA",
-        "weights": "qwen-anime.safetensors",
-        "trigger_word": "Qwen Anime"
+        "thumbnail_url": "https://huggingface.co/prithivMLmods/Qwen-Image-Anime-LoRA/resolve/main/images/1.png",
+        "style_name": "Qwen Anime",
+        "repo_id": "prithivMLmods/Qwen-Image-Anime-LoRA",
+        "weight_file": "qwen-anime.safetensors",
+        "activation_phrase": "Qwen Anime"
     },
     {
-        "image": "https://huggingface.co/prithivMLmods/Qwen-Image-Synthetic-Face/resolve/main/images/2.png",
-        "title": "Synthetic Face",
-        "repo": "prithivMLmods/Qwen-Image-Synthetic-Face",
-        "weights": "qwen-synthetic-face.safetensors",
-        "trigger_word": "Synthetic Face"
+        "thumbnail_url": "https://huggingface.co/prithivMLmods/Qwen-Image-Synthetic-Face/resolve/main/images/2.png",
+        "style_name": "Synthetic Face",
+        "repo_id": "prithivMLmods/Qwen-Image-Synthetic-Face",
+        "weight_file": "qwen-synthetic-face.safetensors",
+        "activation_phrase": "Synthetic Face"
     },
     {
-        "image": "https://huggingface.co/prithivMLmods/Qwen-Image-Fragmented-Portraiture/resolve/main/images/3.png",
-        "title": "Fragmented Portraiture",
-        "repo": "prithivMLmods/Qwen-Image-Fragmented-Portraiture",
-        "weights": "qwen-fragmented-portraiture.safetensors",
-        "trigger_word": "Fragmented Portraiture"
+        "thumbnail_url": "https://huggingface.co/prithivMLmods/Qwen-Image-Fragmented-Portraiture/resolve/main/images/3.png",
+        "style_name": "Fragmented Portraiture",
+        "repo_id": "prithivMLmods/Qwen-Image-Fragmented-Portraiture",
+        "weight_file": "qwen-fragmented-portraiture.safetensors",
+        "activation_phrase": "Fragmented Portraiture"
     },
 ]
 
-# Initialize the base model
-dtype = torch.bfloat16
-device = "cuda" if torch.cuda.is_available() else "cpu"
-base_model = "Qwen/Qwen-Image"
+# --- Model Initialization ---
+model_precision = torch.bfloat16
+processing_device = "cuda" if torch.cuda.is_available() else "cpu"
+foundation_model_id = "Qwen/Qwen-Image"
 
-# Scheduler configuration from the Qwen-Image-Lightning repository
-scheduler_config = {
+# Sampler configuration from the Qwen-Image-Lightning repository
+sampler_settings = {
     "base_image_seq_len": 256,
     "base_shift": math.log(3),
     "invert_sigmas": False,
@@ -89,18 +101,19 @@ scheduler_config = {
     "use_karras_sigmas": False,
 }
 
-scheduler = FlowMatchEulerDiscreteScheduler.from_config(scheduler_config)
-pipe = DiffusionPipeline.from_pretrained(
-    base_model, scheduler=scheduler, torch_dtype=dtype
-).to(device)
+sampler = FlowMatchEulerDiscreteScheduler.from_config(sampler_settings)
+diffusion_pipeline = DiffusionPipeline.from_pretrained(
+    foundation_model_id, scheduler=sampler, torch_dtype=model_precision
+).to(processing_device)
 
-# Lightning LoRA info (no global state)
-LIGHTNING_LORA_REPO = "lightx2v/Qwen-Image-Lightning"
-LIGHTNING_LORA_WEIGHT = "Qwen-Image-Lightning-8steps-V1.0.safetensors"
+# Information for the fast generation LoRA
+FAST_GENERATION_LORA_REPO = "lightx2v/Qwen-Image-Lightning"
+FAST_GENERATION_LORA_WEIGHTS = "Qwen-Image-Lightning-8steps-V1.0.safetensors"
 
-MAX_SEED = np.iinfo(np.int32).max
+MAX_SEED_VALUE = np.iinfo(np.int32).max
 
-class calculateDuration:
+class ExecutionTimer:
+    """A context manager to time a block of code."""
     def __init__(self, activity_name=""):
         self.activity_name = activity_name
 
@@ -111,272 +124,269 @@ class calculateDuration:
     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
-    else:
-        return 1024, 1024
+        activity_log = f" for {self.activity_name}" if self.activity_name else ""
+        print(f"Elapsed time{activity_log}: {self.elapsed_time:.6f} seconds")
 
-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}) ✨"
+def get_dimensions_from_ratio(aspect_ratio_str):
+    """Converts an aspect ratio string to a (width, height) tuple."""
+    ratios = {
+        "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),
+    }
+    return ratios.get(aspect_ratio_str, (1024, 1024))
+
+def on_style_select(event_data: gr.SelectData, current_aspect_ratio):
+    """Handles the user selecting a style from the gallery."""
+    selected_style = style_definitions[event_data.index]
+    new_placeholder = f"Type a prompt for {selected_style['style_name']}"
+    repo_id = selected_style["repo_id"]
+    updated_info_text = f"### Selected: [{repo_id}](https://huggingface.co/{repo_id}) ✨"
     
-    # 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"
+    # Update aspect ratio if specified in the style's configuration
+    if "aspect" in selected_style:
+        if selected_style["aspect"] == "portrait":
+            current_aspect_ratio = "9:16"
+        elif selected_style["aspect"] == "landscape":
+            current_aspect_ratio = "16:9"
         else:
-            aspect_ratio = "1:1"
+            current_aspect_ratio = "1:1"
     
     return (
         gr.update(placeholder=new_placeholder),
-        updated_text,
-        evt.index,
-        aspect_ratio,
+        updated_info_text,
+        event_data.index,
+        current_aspect_ratio,
     )
 
-def handle_speed_mode(speed_mode):
-    """Update UI based on speed/quality toggle."""
-    if speed_mode == "Speed (8 steps)":
+def on_mode_change(generation_mode):
+    """Updates UI elements based on the selected generation mode (Speed/Quality)."""
+    if generation_mode == "Speed (8 steps)":
         return gr.update(value="Speed mode selected - 8 steps with Lightning LoRA"), 8, 1.0
     else: 
         return gr.update(value="Quality mode selected - 45 steps for best quality"), 45, 3.5
 
 @spaces.GPU(duration=70)
-def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, negative_prompt=""):
-    pipe.to("cuda")
-    generator = torch.Generator(device="cuda").manual_seed(seed)
+def execute_image_generation(full_prompt, steps, seed_val, cfg, width, height, negative_prompt=""):
+    """Generates an image using the diffusion pipeline."""
+    diffusion_pipeline.to("cuda")
+    generator = torch.Generator(device="cuda").manual_seed(seed_val)
     
-    with calculateDuration("Generating image"):
-        # Generate image
-        image = pipe(
-            prompt=prompt_mash,
+    with ExecutionTimer("Image Generation"):
+        generated_image = diffusion_pipeline(
+            prompt=full_prompt,
             negative_prompt=negative_prompt,
             num_inference_steps=steps,
-            true_cfg_scale=cfg_scale,  # Use true_cfg_scale for Qwen-Image
+            true_cfg_scale=cfg,
             width=width,
             height=height,
             generator=generator,
         ).images[0]
         
-    return image
+    return generated_image
 
 @spaces.GPU(duration=70)
-def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, aspect_ratio, lora_scale, speed_mode, progress=gr.Progress(track_tqdm=True)):
-    if selected_index is None:
-        raise gr.Error("You must select a LoRA before proceeding.")
+def handle_generate_request(prompt_text, cfg, steps, style_idx, use_random_seed, seed_val, aspect_ratio_str, style_scale, generation_mode, progress=gr.Progress(track_tqdm=True)):
+    """Main function to handle a user's image generation request."""
+    if style_idx is None:
+        raise gr.Error("You must select a style before generating an image.")
     
-    selected_lora = loras[selected_index]
-    lora_path = selected_lora["repo"]
-    trigger_word = selected_lora["trigger_word"]
+    selected_style = style_definitions[style_idx]
+    style_repo_path = selected_style["repo_id"]
+    activation_phrase = selected_style["activation_phrase"]
     
-    # 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}"
+    # Combine the user prompt with the style's activation phrase
+    if activation_phrase:
+        position = selected_style.get("trigger_position", "prepend")
+        if position == "prepend":
+            full_prompt = f"{activation_phrase} {prompt_text}"
         else:
-            prompt_mash = f"{trigger_word} {prompt}"
+            full_prompt = f"{prompt_text} {activation_phrase}"
     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 == "Speed (8 steps)":
-        with calculateDuration("Loading Lightning LoRA and style LoRA"):
-            # Load Lightning LoRA first
-            pipe.load_lora_weights(
-                LIGHTNING_LORA_REPO, 
-                weight_name=LIGHTNING_LORA_WEIGHT,
+        full_prompt = prompt_text
+
+    # Always unload existing adapters to start fresh
+    with ExecutionTimer("Unloading existing adapters"):
+        diffusion_pipeline.unload_lora_weights()
+
+    # Load adapters based on the selected generation mode
+    if generation_mode == "Speed (8 steps)":
+        with ExecutionTimer("Loading Lightning and Style adapters"):
+            # Load the fast generation adapter first
+            diffusion_pipeline.load_lora_weights(
+                FAST_GENERATION_LORA_REPO, 
+                weight_name=FAST_GENERATION_LORA_WEIGHTS,
                 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, 
+            # Load the selected style adapter
+            weight_file = selected_style.get("weight_file", None)
+            diffusion_pipeline.load_lora_weights(
+                style_repo_path, 
+                weight_name=weight_file, 
                 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, 
+            # Set both adapters active with their respective weights
+            diffusion_pipeline.set_adapters(["lightning", "style"], adapter_weights=[1.0, style_scale])
+    else: # Quality mode
+        with ExecutionTimer(f"Loading adapter weights for {selected_style['style_name']}"):
+            weight_file = selected_style.get("weight_file", None)
+            diffusion_pipeline.load_lora_weights(
+                style_repo_path, 
+                weight_name=weight_file, 
                 low_cpu_mem_usage=True
             )
                 
-    # Set random seed for reproducibility
-    with calculateDuration("Randomizing seed"):
-        if randomize_seed:
-            seed = random.randint(0, MAX_SEED)
+    # Set the seed for reproducibility
+    with ExecutionTimer("Setting seed"):
+        if use_random_seed:
+            seed_val = random.randint(0, MAX_SEED_VALUE)
     
-    # Get image dimensions from aspect ratio
-    width, height = get_image_size(aspect_ratio)
+    # Get image dimensions
+    width, height = get_dimensions_from_ratio(aspect_ratio_str)
     
-    # Generate the image
-    final_image = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale)
+    # Generate the final image
+    final_image = execute_image_generation(full_prompt, steps, seed_val, cfg, width, height)
     
-    return final_image, seed
+    return final_image, seed_val
 
-def get_huggingface_safetensors(link):
-    split_link = link.split("/")
+def fetch_hf_safetensors_details(repo_link):
+    """Fetches details of a LoRA from a Hugging Face repository."""
+    split_link = repo_link.split("/")
     if len(split_link) != 2:
-        raise Exception("Invalid Hugging Face repository link format.")
+        raise ValueError("Invalid Hugging Face repository link format.")
 
-    print(f"Repository attempted: {split_link}")
+    print(f"Attempting to load repository: {repo_link}")
     
-    # Load model card
-    model_card = ModelCard.load(link)
+    model_card = ModelCard.load(repo_link)
     base_model = model_card.data.get("base_model")
-    print(f"Base model: {base_model}")
+    print(f"Base model identified: {base_model}")
 
-    # Validate model type (for Qwen-Image)
+    # Validate that the LoRA is compatible with 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!")
+        raise TypeError("The provided model is not a Qwen-Image compatible 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
+    # Extract metadata from the model card
+    image_path = model_card.data.get("widget", [{}])[0].get("output", {}).get("url")
+    activation_phrase = model_card.data.get("instance_prompt", "")
+    image_url = f"https://huggingface.co/{repo_link}/resolve/main/{image_path}" if image_path else None
 
-    # Initialize Hugging Face file system
+    # Find the .safetensors file in the repository
     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]
+        repo_files = fs.ls(repo_link, detail=False)
+        safetensors_filename = None
+        for file_path in repo_files:
+            filename = file_path.split("/")[-1]
             if filename.endswith(".safetensors"):
-                safetensors_name = filename
+                safetensors_filename = filename
                 break
-
-        if not safetensors_name:
-            raise Exception("No valid *.safetensors file found in the repository.")
-
+        if not safetensors_filename:
+            raise FileNotFoundError("No .safetensors file was 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")
+        raise IOError("Could not access the Hugging Face repository or find a valid .safetensors file.")
     
-    return split_link[1], link, safetensors_name, trigger_word, image_url
+    return split_link[1], repo_link, safetensors_filename, activation_phrase, image_url
 
-def check_custom_model(link):
-    print(f"Checking a custom model on: {link}")
+def parse_custom_model_source(source_text):
+    """Parses a user-provided link to a custom LoRA."""
+    print(f"Parsing custom model source: {source_text}")
     
-    if link.endswith('.safetensors'):
-        if 'huggingface.co' in link:
-            parts = link.split('/')
+    if source_text.endswith('.safetensors') and 'huggingface.co' in source_text:
+        parts = source_text.split('/')
+        try:
+            hf_index = parts.index('huggingface.co')
+            username = parts[hf_index + 1]
+            repo_name = parts[hf_index + 2]
+            repo_id = f"{username}/{repo_name}"
+            safetensors_filename = parts[-1]
+            
             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")
+                model_card = ModelCard.load(repo_id)
+                activation_phrase = model_card.data.get("instance_prompt", "")
+                image_path = model_card.data.get("widget", [{}])[0].get("output", {}).get("url")
+                image_url = f"https://huggingface.co/{repo_id}/resolve/main/{image_path}" if image_path else None
+            except Exception:
+                activation_phrase = ""
+                image_url = None
+            
+            return repo_name, repo_id, safetensors_filename, activation_phrase, image_url
+        except ValueError:
+            raise ValueError("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)
+    if source_text.startswith("https://"):
+        parsed_url = urlparse(source_text)
+        if "huggingface.co" in parsed_url.netloc:
+            repo_link = parsed_url.path.strip("/")
+            return fetch_hf_safetensors_details(repo_link)
+    
+    # Assume it's a direct repo path like "username/repo-name"
+    return fetch_hf_safetensors_details(source_text)
 
-def add_custom_lora(custom_lora):
-    global loras
-    if custom_lora:
+
+def add_custom_style_model(custom_model_path):
+    """Adds a custom LoRA provided by the user to the session."""
+    global style_definitions
+    if custom_model_path:
         try:
-            title, repo, path, trigger_word, image = check_custom_model(custom_lora)
-            print(f"Loaded custom LoRA: {repo}")
-            card = f'''
+            style_name, repo_id, weight_file, activation_phrase, thumbnail_url = parse_custom_model_source(custom_model_path)
+            print(f"Successfully loaded custom style: {repo_id}")
+            
+            card_html = f'''
             <div class="custom_lora_card">
-              <span>Loaded custom LoRA:</span>
+              <span>Loaded custom style:</span>
               <div class="card_internal">
-                <img src="{image}" />
+                <img src="{thumbnail_url}" alt="{style_name}" />
                 <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>
+                    <h3>{style_name}</h3>
+                    <small>{"Activation phrase: <code><b>"+activation_phrase+"</b></code>" if activation_phrase else "No activation phrase found. If required, include it in your prompt."}<br></small>
                 </div>
               </div>
             </div>
             '''
-            existing_item_index = next((index for (index, item) in enumerate(loras) if item['repo'] == repo), None)
+            
+            # Check if this style already exists
+            existing_item_index = next((index for (index, item) in enumerate(style_definitions) if item['repo_id'] == repo_id), None)
+            
             if existing_item_index is None:
-                new_item = {
-                    "image": image,
-                    "title": title,
-                    "repo": repo,
-                    "weights": path,
-                    "trigger_word": trigger_word
+                new_style_item = {
+                    "thumbnail_url": thumbnail_url,
+                    "style_name": style_name,
+                    "repo_id": repo_id,
+                    "weight_file": weight_file,
+                    "activation_phrase": activation_phrase
                 }
-                print(new_item)
-                loras.append(new_item)
-                existing_item_index = len(loras) - 1  # Get the actual index after adding
+                style_definitions.append(new_style_item)
+                existing_item_index = len(style_definitions) - 1
+        
+            return gr.update(visible=True, value=card_html), gr.update(visible=True), gr.Gallery(selected_index=None), f"Custom: {weight_file}", existing_item_index, activation_phrase
         
-            return gr.update(visible=True, value=card), gr.update(visible=True), gr.Gallery(selected_index=None), f"Custom: {path}", existing_item_index, trigger_word
         except Exception as e:
-            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, ""
-    else:
-        return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""
+            gr.Warning(f"Failed to load custom style. Error: {e}")
+            error_message = f"Invalid input. Could not load the specified style. Please check the link or repository path."
+            return gr.update(visible=True, value=error_message), gr.update(visible=True), gr.update(), "", None, ""
+    
+    # If input is empty, hide the custom section
+    return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""
 
-def remove_custom_lora():
+def remove_custom_style_model():
+    """Resets the UI when a custom LoRA is removed."""
     return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""
 
-run_lora.zerogpu = True
 
-css = '''
+# --- Gradio UI Definition ---
+
+app_css = '''
 #gen_btn{height: 100%}
 #gen_column{align-self: stretch}
 #title{text-align: center}
@@ -385,112 +395,115 @@ css = '''
 #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}
+.card_internal img{margin-right: 1em; object-fit: cover;}
 .styler{--form-gap-width: 0px !important}
 #speed_status{padding: .5em; border-radius: 5px; margin: 1em 0}
+.custom_lora_card{padding: 1em; border: 1px solid var(--border-color-primary); border-radius: var(--radius-lg)}
 '''
 
-with gr.Blocks(theme="bethecloud/storj_theme", css=css, delete_cache=(120, 120)) as app:
-    title = gr.HTML("""<h1>Qwen Image LoRA DLC ❤️‍🔥</h1>""", elem_id="title")
-    selected_index = gr.State(None)
+with gr.Blocks(theme="bethecloud/storj_theme", css=app_css, delete_cache=(120, 120)) as web_interface:
+    main_title = gr.HTML("""<h1>Qwen Image Style Showcase ❤️‍🔥</h1>""", elem_id="title")
+    selected_style_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")
+            prompt_textbox = gr.Textbox(label="Prompt", lines=1, placeholder="Select a style to begin...")
         with gr.Column(scale=1, elem_id="gen_column"):
-            generate_button = gr.Button("Generate", variant="primary", elem_id="gen_btn")
+            generate_btn = gr.Button("Generate", variant="primary", elem_id="gen_btn")
     
     with gr.Row():
         with gr.Column():
-            selected_info = gr.Markdown("")
-            gallery = gr.Gallery(
-                [(item["image"], item["title"]) for item in loras],
-                label="LoRA Gallery",
+            selected_style_info = gr.Markdown("")
+            style_gallery = gr.Gallery(
+                [(item["thumbnail_url"], item["style_name"]) for item in style_definitions],
+                label="Style 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)
+                custom_style_textbox = gr.Textbox(label="Load Custom Style", info="Enter a Hugging Face repository path (e.g., username/repo-name)", placeholder="username/qwen-image-custom-style")
+                gr.Markdown("[Find More Qwen-Image Styles Here](https://huggingface.co/models?other=base_model:adapter:Qwen/Qwen-Image)", elem_id="lora_list")
+            custom_style_info_html = gr.HTML(visible=False)
+            remove_custom_style_btn = gr.Button("Remove Custom Style", visible=False)
         
         with gr.Column():
-            result = gr.Image(label="Generated Image")
+            output_image_display = gr.Image(label="Generated Image")
 
             with gr.Row():
-                aspect_ratio = gr.Dropdown(
+                aspect_ratio_dropdown = gr.Dropdown(
                     label="Aspect Ratio",
                     choices=["1:1", "16:9", "9:16", "4:3", "3:4", "3:2", "2:3"],
                     value="1:1"
-                    )
+                )
             with gr.Row():
-                speed_mode = gr.Dropdown(
+                generation_mode_dropdown = gr.Dropdown(
                     label="Generation Mode",
                     choices=["Speed (8 steps)", "Quality (45 steps)"],
-                    value="Quality (48 steps)",
+                    value="Quality (45 steps)",
                 )
             
-            speed_status = gr.Markdown("Quality mode active", elem_id="speed_status")
+            generation_mode_status_display = gr.Markdown("Quality mode active", elem_id="speed_status")
 
     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)", 
+                    cfg_scale_slider = gr.Slider(
+                        label="Guidance Scale (CFG)", 
                         minimum=1.0, 
                         maximum=5.0, 
                         step=0.1, 
                         value=3.5,
-                        info="Lower for speed mode, higher for quality"
+                        info="Adjusts how strictly the model follows the prompt. Lower for speed, higher for quality."
                     )
-                    steps = gr.Slider(
-                        label="Steps", 
+                    steps_slider = gr.Slider(
+                        label="Inference Steps", 
                         minimum=4, 
                         maximum=50, 
                         step=1, 
                         value=45,
-                        info="Automatically set by speed mode"
+                        info="Number of steps for the generation process. Automatically set by Generation 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=2, step=0.01, value=1.0)
-
-    # Event handlers
-    gallery.select(
-        update_selection,
-        inputs=[aspect_ratio],
-        outputs=[prompt, selected_info, selected_index, aspect_ratio]
+                    randomize_seed_checkbox = gr.Checkbox(True, label="Use Random Seed")
+                    seed_slider = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED_VALUE, step=1, value=0, randomize=True)
+                    style_scale_slider = gr.Slider(label="Style Strength", minimum=0, maximum=2, step=0.01, value=1.0)
+
+    # --- Event Handlers ---
+    style_gallery.select(
+        on_style_select,
+        inputs=[aspect_ratio_dropdown],
+        outputs=[prompt_textbox, selected_style_info, selected_style_index, aspect_ratio_dropdown]
     )
     
-    speed_mode.change(
-        handle_speed_mode,
-        inputs=[speed_mode],
-        outputs=[speed_status, steps, cfg_scale]
+    generation_mode_dropdown.change(
+        on_mode_change,
+        inputs=[generation_mode_dropdown],
+        outputs=[generation_mode_status_display, steps_slider, cfg_scale_slider]
     )
     
-    custom_lora.input(
-        add_custom_lora,
-        inputs=[custom_lora],
-        outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, prompt]
+    custom_style_textbox.submit(
+        add_custom_style_model,
+        inputs=[custom_style_textbox],
+        outputs=[custom_style_info_html, remove_custom_style_btn, style_gallery, selected_style_info, selected_style_index, prompt_textbox]
     )
     
-    custom_lora_button.click(
-        remove_custom_lora,
-        outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, custom_lora]
+    remove_custom_style_btn.click(
+        remove_custom_style_model,
+        outputs=[custom_style_info_html, remove_custom_style_btn, style_gallery, selected_style_info, selected_style_index, custom_style_textbox]
     )
     
+    # Combined trigger for generation
+    generate_triggers = [generate_btn.click, prompt_textbox.submit]
     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],
-        outputs=[result, seed]
+        triggers=generate_triggers,
+        fn=handle_generate_request,
+        inputs=[prompt_textbox, cfg_scale_slider, steps_slider, selected_style_index, randomize_seed_checkbox, seed_slider, aspect_ratio_dropdown, style_scale_slider, generation_mode_dropdown],
+        outputs=[output_image_display, seed_slider]
     )
 
-app.queue()
-app.launch(share=False, ssr_mode=False, show_error=True)
+web_interface.queue()
+web_interface.launch(share=False, ssr_mode=False, show_error=True)