Update app.py

app.py

@@ -24,31 +24,6 @@ import shutil
 import uuid
 import zipfile
 
-# Helper functions
-def save_image(img):
-    unique_name = str(uuid.uuid4()) + ".png"
-    img.save(unique_name)
-    return unique_name
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 2048
-
-# Load Qwen/Qwen-Image pipeline
-dtype = torch.bfloat16
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-# Load Qwen model
-pipe = DiffusionPipeline.from_pretrained("Qwen/Qwen-Image", torch_dtype=dtype).to(device)
-
-# Aspect ratios
-aspect_ratios = {
-    "1:1": (1328, 1328),
-    "16:9": (1664, 928),
-    "9:16": (928, 1664),
-    "4:3": (1472, 1140),
-    "3:4": (1140, 1472)
-}
-
 loras = [
     # Sample Qwen-compatible LoRAs
     {
@@ -88,84 +63,271 @@ loras = [
     },
 ]
 
-def load_lora_opt(pipe, lora_input):
-    lora_input = lora_input.strip()
-    if not lora_input:
-        return
+# 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-8steps-V1.0.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
     
-    # If it's just an ID like "author/model"
-    if "/" in lora_input and not lora_input.startswith("http"):
-        pipe.load_lora_weights(lora_input, adapter_name="default")
-        return
+    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
+
+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}) ✨"
     
-    if lora_input.startswith("http"):
-        url = lora_input
-        # Repo page (no blob/resolve)
-        if "huggingface.co" in url and "/blob/" not in url and "/resolve/" not in url:
-            repo_id = urlparse(url).path.strip("/")
-            pipe.load_lora_weights(repo_id, adapter_name="default")
-            return
-        
-        # Blob link → convert to resolve link
-        if "/blob/" in url:
-            url = url.replace("/blob/", "/resolve/")
+    # 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"
+        else:
+            aspect_ratio = "1:1"
+    
+    return (
+        gr.update(placeholder=new_placeholder),
+        updated_text,
+        evt.index,
+        aspect_ratio,
+    )
+
+def handle_speed_mode(speed_mode):
+    """Update UI based on speed/quality toggle."""
+    if speed_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)
+    
+    with calculateDuration("Generating image"):
+        # Generate image
+        image = pipe(
+            prompt=prompt_mash,
+            negative_prompt=negative_prompt,
+            num_inference_steps=steps,
+            true_cfg_scale=cfg_scale,  # Use true_cfg_scale for Qwen-Image
+            width=width,
+            height=height,
+            generator=generator,
+        ).images[0]
         
-        # Download direct file
-        tmp_dir = tempfile.mkdtemp()
-        local_path = os.path.join(tmp_dir, os.path.basename(urlparse(url).path))
-        try:
-            print(f"Downloading LoRA from {url}...")
-            resp = requests.get(url, stream=True)
-            resp.raise_for_status()
-            with open(local_path, "wb") as f:
-                for chunk in resp.iter_content(chunk_size=8192):
-                    f.write(chunk)
-            print(f"Saved LoRA to {local_path}")
-            pipe.load_lora_weights(local_path, adapter_name="default")
-        finally:
-            shutil.rmtree(tmp_dir, ignore_errors=True)
+    return image
 
-def get_huggingface_safetensors(link):
-    split_link = link.split("/")
-    if len(split_link) == 2:
-        try:
-            response = requests.get(f"https://huggingface.co/api/models/{link}")
-            response.raise_for_status()
-            model_info = response.json()
+@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.")
+    
+    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 == "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,
+                adapter_name="lightning"
+            )
             
-            # Check if it's a Qwen model
-            if "qwen" not in model_info.get("tags", []):
-                raise Exception("Not a Qwen LoRA model!")
-                
-            # Get image if available
-            image_url = None
-            if "cardData" in model_info and "widget" in model_info["cardData"]:
-                if len(model_info["cardData"]["widget"]) > 0:
-                    image_url = model_info["cardData"]["widget"][0].get("output", {}).get("url", None)
+            # 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"
+            )
             
-            # Try to find safetensors file
-            safetensors_name = None
-            try:
-                model_files = requests.get(f"https://huggingface.co/api/models/{link}/tree/main").json()
-                for file in model_files:
-                    if file.get("path", "").endswith(".safetensors"):
-                        safetensors_name = file["path"]
-                        break
-            except:
-                pass
+            # 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
+            )
                 
-            return split_link[1], link, safetensors_name, "trigger_word", image_url
-        except Exception as e:
-            print(f"Error getting model info: {e}")
-            raise Exception(f"Failed to get model info: {e}")
-    return None, None, None, None, None
+    # 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)
+    
+    # Generate the image
+    final_image = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale)
+    
+    return final_image, 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:
+    else: 
         return get_huggingface_safetensors(link)
 
 def add_custom_lora(custom_lora):
@@ -173,9 +335,6 @@ def add_custom_lora(custom_lora):
     if custom_lora:
         try:
             title, repo, path, trigger_word, image = check_custom_model(custom_lora)
-            if not title:
-                raise Exception("Invalid LoRA model")
-                
             print(f"Loaded custom LoRA: {repo}")
             card = f'''
             <div class="custom_lora_card">
@@ -189,9 +348,8 @@ def add_custom_lora(custom_lora):
               </div>
             </div>
             '''
-            
             existing_item_index = next((index for (index, item) in enumerate(loras) if item['repo'] == repo), None)
-            if not existing_item_index:
+            if existing_item_index is None:
                 new_item = {
                     "image": image,
                     "title": title,
@@ -199,161 +357,21 @@ def add_custom_lora(custom_lora):
                     "weights": path,
                     "trigger_word": trigger_word
                 }
-                existing_item_index = len(loras)
+                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
         except Exception as e:
-            gr.Warning(f"Invalid LoRA: either you entered an invalid link, or a non-Qwen LoRA")
-            return gr.update(visible=True, value=f"Invalid LoRA: either you entered an invalid link, a non-Qwen LoRA"), gr.update(visible=False), gr.update(), "", None, ""
+            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, ""
 
 def remove_custom_lora():
     return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""
 
-def update_selection(evt: gr.SelectData, width, height):
-    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}) ✅"
-    
-    # Update aspect ratio based on LoRA if it has aspect info
-    if "aspect" in selected_lora:
-        if selected_lora["aspect"] == "portrait":
-            width = 928
-            height = 1664
-        elif selected_lora["aspect"] == "landscape":
-            width = 1664
-            height = 928
-        else:
-            width = 1328
-            height = 1328
-    
-    return (
-        gr.update(placeholder=new_placeholder),
-        updated_text,
-        evt.index,
-        width,
-        height,
-    )
-
-@spaces.GPU(duration=120)
-def generate_qwen(
-    prompt: str,
-    negative_prompt: str = "",
-    seed: int = 0,
-    width: int = 1024,
-    height: int = 1024,
-    guidance_scale: float = 4.0,
-    randomize_seed: bool = False,
-    num_inference_steps: int = 50,
-    num_images: int = 1,
-    zip_images: bool = False,
-    lora_input: str = "",
-    lora_scale: float = 1.0,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    
-    generator = torch.Generator(device).manual_seed(seed)
-    
-    start_time = time.time()
-    
-    # Clear any existing LoRA adapters
-    current_adapters = pipe.get_list_adapters()
-    for adapter in current_adapters:
-        pipe.delete_adapters(adapter)
-    pipe.disable_lora()
-    
-    use_lora = False
-    if lora_input and lora_input.strip() != "":
-        load_lora_opt(pipe, lora_input)
-        pipe.set_adapters(["default"], adapter_weights=[lora_scale])
-        use_lora = True
-    
-    images = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt if negative_prompt else "",
-        height=height,
-        width=width,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        num_images_per_prompt=num_images,
-        generator=generator,
-        output_type="pil",
-    ).images
-    
-    end_time = time.time()
-    duration = end_time - start_time
-    
-    image_paths = [save_image(img) for img in images]
-    zip_path = None
-    if zip_images:
-        zip_name = str(uuid.uuid4()) + ".zip"
-        with zipfile.ZipFile(zip_name, 'w') as zipf:
-            for i, img_path in enumerate(image_paths):
-                zipf.write(img_path, arcname=f"Img_{i}.png")
-        zip_path = zip_name
-    
-    # Clean up adapters
-    current_adapters = pipe.get_list_adapters()
-    for adapter in current_adapters:
-        pipe.delete_adapters(adapter)
-    pipe.disable_lora()
-    
-    return image_paths, seed, f"{duration:.2f}", zip_path
-
-@spaces.GPU(duration=120)
-def run_lora(
-    prompt: str,
-    negative_prompt: str,
-    use_negative_prompt: bool,
-    seed: int,
-    width: int,
-    height: int,
-    guidance_scale: float,
-    randomize_seed: bool,
-    num_inference_steps: int,
-    num_images: int,
-    zip_images: bool,
-    selected_index: int,
-    lora_scale: float,
-    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_repo = selected_lora["repo"]
-    trigger_word = selected_lora["trigger_word"]
-    
-    if trigger_word:
-        prompt_mash = f"{trigger_word} {prompt}"
-    else:
-        prompt_mash = prompt
-    
-    final_negative_prompt = negative_prompt if use_negative_prompt else ""
-    
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    
-    return generate_qwen(
-        prompt=prompt_mash,
-        negative_prompt=final_negative_prompt,
-        seed=seed,
-        width=width,
-        height=height,
-        guidance_scale=guidance_scale,
-        randomize_seed=False,  # Already handled
-        num_inference_steps=num_inference_steps,
-        num_images=num_images,
-        zip_images=zip_images,
-        lora_input=lora_repo,
-        lora_scale=lora_scale,
-        progress=progress,
-    )
+run_lora.zerogpu = True
 
 css = '''
 #gen_btn{height: 100%}
@@ -366,10 +384,7 @@ css = '''
 .card_internal{display: flex;height: 100px;margin-top: .5em}
 .card_internal img{margin-right: 1em}
 .styler{--form-gap-width: 0px !important}
-#progress{height:30px}
-#progress .generating{display:none}
-.progress-container {width: 100%;height: 30px;background-color: #f0f0f0;border-radius: 15px;overflow: hidden;margin-bottom: 20px}
-.progress-bar {height: 100%;background-color: #4f46e5;width: calc(var(--current) / var(--total) * 100%);transition: width 0.5s ease-in-out}
+#speed_status{padding: .5em; border-radius: 5px; margin: 1em 0}
 '''
 
 with gr.Blocks(theme="bethecloud/storj_theme", css=css, delete_cache=(120, 120)) as app:
@@ -378,7 +393,7 @@ with gr.Blocks(theme="bethecloud/storj_theme", css=css, delete_cache=(120, 120))
     
     with gr.Row():
         with gr.Column(scale=3):
-            prompt = gr.Textbox(label="Prompt", lines=1, placeholder="✦︎ Choose the LoRA and type the prompt")
+            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")
     
@@ -387,89 +402,73 @@ with gr.Blocks(theme="bethecloud/storj_theme", css=css, delete_cache=(120, 120))
             selected_info = gr.Markdown("")
             gallery = gr.Gallery(
                 [(item["image"], item["title"]) for item in loras],
-                label="Qwen LoRA DLC's",
+                label="LoRA Gallery",
                 allow_preview=False,
                 columns=3,
                 elem_id="gallery",
                 show_share_button=False
             )
             with gr.Group():
-                custom_lora = gr.Textbox(label="Enter Custom LoRA", placeholder="prithivMLmods/Qwen-Image-Sketch-Smudge")
-                gr.Markdown("[Check the list of Qwen LoRA's](https://huggingface.co/models?other=base_model:adapter:Qwen/Qwen-Image)", elem_id="lora_list")
+                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", columns=1, show_label=False, preview=True)
+            result = gr.Image(label="Generated Image")
+
             with gr.Row():
                 aspect_ratio = gr.Dropdown(
                     label="Aspect Ratio",
-                    choices=list(aspect_ratios.keys()),
-                    value="1:1",
-                )
+                    choices=["1:1", "16:9", "9:16", "4:3", "3:4", "3:2", "2:3"],
+                    value="1:1"
+                    )
             with gr.Row():
-                steps = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=48)
-
-    with gr.Row():
-        with gr.Accordion("Advanced Settings", open=False):
-            
-            with gr.Row():
-                use_negative_prompt = gr.Checkbox(
-                    label="Use negative prompt",
-                    value=True,
+                speed_mode = gr.Dropdown(
+                    label="Generation Mode",
+                    choices=["Speed (8 steps)", "Quality (45 steps)"],
+                    value="Quality (48 steps)",
                 )
-                negative_prompt = gr.Text(
-                    label="Negative prompt",
-                    max_lines=1,
-                    placeholder="Enter a negative prompt",
-                    value="text, watermark, copyright, blurry, low resolution",
-                )
-            
-            with gr.Row():
-                cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=4.0)
-                steps = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=50)
-            
-            with gr.Row():
-                width = gr.Slider(label="Width", minimum=512, maximum=2048, step=64, value=1328)
-                height = gr.Slider(label="Height", minimum=512, maximum=2048, step=64, value=1328)
-            
-            with gr.Row():
-                num_images = gr.Slider(label="Number of Images", minimum=1, maximum=5, step=1, value=1)
-                zip_images = gr.Checkbox(label="Zip generated images", value=False)
             
-            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)
-    
-    # Output information
+            speed_status = gr.Markdown("Quality mode active", elem_id="speed_status")
+
     with gr.Row():
-        seed_display = gr.Textbox(label="Seed used", interactive=False)
-        generation_time = gr.Textbox(label="Generation time (seconds)", interactive=False)
-        zip_file = gr.File(label="Download ZIP")
-    
-    # Update aspect ratio
-    def set_dimensions(ar):
-        w, h = aspect_ratios[ar]
-        return gr.update(value=w), gr.update(value=h)
-    
-    aspect_ratio.change(
-        fn=set_dimensions,
-        inputs=aspect_ratio,
-        outputs=[width, height]
-    )
-    
-    # Negative prompt visibility
-    use_negative_prompt.change(
-        fn=lambda x: gr.update(visible=x),
-        inputs=use_negative_prompt,
-        outputs=negative_prompt
-    )
-    
+        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=2, step=0.01, value=1.0)
+
+    # Event handlers
     gallery.select(
         update_selection,
-        inputs=[width, height],
-        outputs=[prompt, selected_info, selected_index, width, height]
+        inputs=[aspect_ratio],
+        outputs=[prompt, selected_info, selected_index, aspect_ratio]
+    )
+    
+    speed_mode.change(
+        handle_speed_mode,
+        inputs=[speed_mode],
+        outputs=[speed_status, steps, cfg_scale]
     )
     
     custom_lora.input(
@@ -486,22 +485,8 @@ with gr.Blocks(theme="bethecloud/storj_theme", css=css, delete_cache=(120, 120))
     gr.on(
         triggers=[generate_button.click, prompt.submit],
         fn=run_lora,
-        inputs=[
-            prompt,
-            negative_prompt,
-            use_negative_prompt,
-            seed,
-            width,
-            height,
-           #guidance_scale,
-            randomize_seed,
-            steps,
-            num_images,
-            zip_images,
-            selected_index,
-            lora_scale,
-        ],
-        outputs=[result, seed_display, generation_time, zip_file]
+        inputs=[prompt, cfg_scale, steps, selected_index, randomize_seed, seed, aspect_ratio, lora_scale, speed_mode],
+        outputs=[result, seed]
     )
 
 app.queue()