updates

.gitignore

@@ -0,0 +1 @@
+/backup

Flux-LoRA-Generation-Advanced.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2537b224b8b98c72939afaf580fd85d9c375d1f6e1f94b1c2f630f22fc0f03ce
+size 26901

flux_app/backend.py

@@ -7,8 +7,8 @@ from diffusers import (
     AutoPipelineForImage2Image,
 )
 from flux_app.config import DTYPE, DEVICE, BASE_MODEL, TAEF1_MODEL, MAX_SEED  # Absolute import
-from flux_app.utilities import calculate_shift, retrieve_timesteps, load_image_from_path, calculateDuration # Absolute import
-from flux_app.lora_handling import  flux_pipe_call_that_returns_an_iterable_of_images # Absolute import
+from flux_app.utilities import calculate_shift, retrieve_timesteps, load_image_from_path, calculateDuration  # Absolute import
+from flux_app.lora_handling import flux_pipe_call_that_returns_an_iterable_of_images  # Absolute import
 import time
 from huggingface_hub import login
 
@@ -21,17 +21,14 @@ class ModelManager:
 
         if hf_token:
             login(token=hf_token)  # Log in with the provided token
-        #else:  # Optional:  You could add a fallback to interactive login
-        #   login()
 
         self.initialize_models()
 
-
     def initialize_models(self):
         """Initializes the diffusion pipelines and autoencoders."""
-        self.taef1 = AutoencoderTiny.from_pretrained(TAEF1_MODEL, torch_dtype=DTYPE, token=True).to(DEVICE)
-        self.good_vae = AutoencoderKL.from_pretrained(BASE_MODEL, subfolder="vae", torch_dtype=DTYPE, token=True).to(DEVICE)
-        self.pipe = DiffusionPipeline.from_pretrained(BASE_MODEL, torch_dtype=DTYPE, vae=self.taef1, token=True).to(DEVICE)
+        self.taef1 = AutoencoderTiny.from_pretrained(TAEF1_MODEL, torch_dtype=DTYPE).to(DEVICE)
+        self.good_vae = AutoencoderKL.from_pretrained(BASE_MODEL, subfolder="vae", torch_dtype=DTYPE).to(DEVICE)
+        self.pipe = DiffusionPipeline.from_pretrained(BASE_MODEL, torch_dtype=DTYPE, vae=self.taef1).to(DEVICE)
         self.pipe_i2i = AutoPipelineForImage2Image.from_pretrained(
             BASE_MODEL,
             vae=self.good_vae,
@@ -40,18 +37,17 @@ class ModelManager:
             tokenizer=self.pipe.tokenizer,
             text_encoder_2=self.pipe.text_encoder_2,
             tokenizer_2=self.pipe.tokenizer_2,
-            torch_dtype=DTYPE,
-            token=True
+            torch_dtype=DTYPE
         )
-        self.pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(self.pipe)
 
+        setattr(self.pipe, "flux_pipe_call_that_returns_an_iterable_of_images",
+                lambda *args, **kwargs: flux_pipe_call_that_returns_an_iterable_of_images(self.pipe, *args, **kwargs))
 
     def generate_image(self, prompt_mash, steps, seed, cfg_scale, width, height, lora_scale):
         """Generates an image using the text-to-image pipeline."""
         self.pipe.to(DEVICE)
         generator = torch.Generator(device=DEVICE).manual_seed(seed)
         with calculateDuration("Generating image"):
-
             for img in self.pipe.flux_pipe_call_that_returns_an_iterable_of_images(
                 prompt=prompt_mash,
                 num_inference_steps=steps,
@@ -83,4 +79,4 @@ class ModelManager:
                 joint_attention_kwargs={"scale": lora_scale},
                 output_type="pil",
             ).images[0]
-            return final_image
+            return final_image

flux_app/enhance_v1.py

@@ -1,56 +0,0 @@
-# flux_app/enhance.py
-import time
-from huggingface_hub import InferenceClient
-import gradio as gr
-
-# Initialize the inference client with the new LLM
-client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
-
-# Define the system prompt for enhancing user prompts
-SYSTEM_PROMPT = (
-    "You are a prompt enhancer and your work is to enhance the given prompt under 100 words "
-    "without changing the essence, only write the enhanced prompt and nothing else."
-)
-
-def format_prompt(message):
-    """
-    Format the input message using the system prompt and a timestamp to ensure uniqueness.
-    """
-    timestamp = time.time()
-    formatted = (
-        f"<s>[INST] SYSTEM: {SYSTEM_PROMPT} [/INST]"
-        f"[INST] {message} {timestamp} [/INST]"
-    )
-    return formatted
-
-def generate(message, max_new_tokens=256, temperature=0.9, top_p=0.95, repetition_penalty=1.0):
-    """
-    Generate an enhanced prompt using the new LLM.
-    This function yields intermediate results as they are generated.
-    """
-    temperature = float(temperature)
-    if temperature < 1e-2:
-        temperature = 1e-2
-    top_p = float(top_p)
-    generate_kwargs = {
-        "temperature": temperature,
-        "max_new_tokens": int(max_new_tokens),
-        "top_p": top_p,
-        "repetition_penalty": float(repetition_penalty),
-        "do_sample": True,
-    }
-    formatted_prompt = format_prompt(message)
-    stream = client.text_generation(
-        formatted_prompt,
-        **generate_kwargs,
-        stream=True,
-        details=True,
-        return_full_text=False,
-    )
-    output = ""
-    for response in stream:
-        token_text = response.token.text
-        output += token_text
-        yield output.strip('</s>')
-    return output.strip('</s>')
-

flux_app/enhance_v2.py

@@ -1,55 +0,0 @@
-# flux_app/enhance.py
-import time
-from huggingface_hub import InferenceClient
-import gradio as gr
-
-# Initialize the inference client with the new LLM
-client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
-
-# Define the system prompt for enhancing user prompts
-SYSTEM_PROMPT = (
-    "You are a prompt enhancer and your work is to enhance the given prompt under 100 words "
-    "without changing the essence, only write the enhanced prompt and nothing else."
-)
-
-def format_prompt(message):
-    """
-    Format the input message using the system prompt and a timestamp to ensure uniqueness.
-    """
-    timestamp = time.time()
-    formatted = (
-        f"<s>[INST] SYSTEM: {SYSTEM_PROMPT} [/INST]"
-        f"[INST] {message} {timestamp} [/INST]"
-    )
-    return formatted
-
-def generate(message, max_new_tokens=256, temperature=0.9, top_p=0.95, repetition_penalty=1.0):
-    """
-    Generate an enhanced prompt using the new LLM.
-    This function yields intermediate results as they are generated.
-    """
-    temperature = float(temperature)
-    if temperature < 1e-2:
-        temperature = 1e-2
-    top_p = float(top_p)
-    generate_kwargs = {
-        "temperature": temperature,
-        "max_new_tokens": int(max_new_tokens),
-        "top_p": top_p,
-        "repetition_penalty": float(repetition_penalty),
-        "do_sample": True,
-    }
-    formatted_prompt = format_prompt(message)
-    stream = client.text_generation(
-        formatted_prompt,
-        **generate_kwargs,
-        stream=True,
-        details=True,
-        return_full_text=False,
-    )
-    output = ""
-    for response in stream:
-        token_text = response.token.text
-        output += token_text
-        yield output.strip('</s>')
-    return output.strip('</s>')

flux_app/frontend.py

@@ -103,7 +103,7 @@ class Frontend:
             print("Warning: lora.py not found, using placeholder LoRAs.")
             pass
 
-    @spaces.GPU(duration=100)
+    @spaces.GPU(duration=300)
     def run_lora(self, prompt, image_input, image_strength, cfg_scale, steps, selected_index,
                  randomize_seed, seed, width, height, lora_scale, use_enhancer,
                  progress=gr.Progress(track_tqdm=True)):

flux_app/frontend_nw.py

@@ -1,236 +0,0 @@
-# frontend.py
-import gradio as gr
-import sys
-import os
-import spaces
-# Add the parent directory to sys.path
-parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
-sys.path.insert(0, parent_dir)
-#print(sys.path) #DEBUG
-
-from flux_app.backend import ModelManager  # Absolute import
-from flux_app.config import MAX_SEED      # Absolute import
-from flux_app.lora_handling import (
-    add_custom_lora, remove_custom_lora, prepare_prompt,
-    unload_lora_weights, load_lora_weights_into_pipeline, update_selection
-)
-from flux_app.utilities import randomize_seed_if_needed, calculateDuration  # Absolute import
-# Import the prompt enhancer generate function from the new module
-from flux_app.enhance import generate
-
-# Dummy loras data for initial UI setup.
-initial_loras = [
-    {"image": "placeholder.jpg", "title": "Placeholder LoRA", "repo": "placeholder/repo", "weights": None, "trigger_word": ""},
-]
-
-class Frontend:
-    def __init__(self, model_manager: ModelManager):
-        self.model_manager = model_manager
-        self.loras = initial_loras
-        self.load_initial_loras()
-        self.css = self.define_css()
-
-    def define_css(self):
-        # A cleaner, professional CSS styling.
-        return '''
-        /* Title Styling */
-        #title {
-            text-align: center;
-            margin-bottom: 20px;
-        }
-        #title h1 {
-            font-size: 2.5rem;
-            margin: 0;
-            color: #333;
-        }
-        /* Button and Column Styling */
-        #gen_btn {
-            width: 100%;
-            padding: 12px;
-            font-weight: bold;
-            border-radius: 5px;
-        }
-        #gen_column {
-            display: flex;
-            align-items: center;
-            justify-content: center;
-        }
-        /* Gallery and List Styling */
-        #gallery .grid-wrap {
-            margin-top: 15px;
-        }
-        #lora_list {
-            background-color: #f5f5f5;
-            padding: 10px;
-            border-radius: 4px;
-            font-size: 0.9rem;
-        }
-        .card_internal {
-            display: flex;
-            align-items: center;
-            height: 100px;
-            margin-top: 10px;
-        }
-        .card_internal img {
-            margin-right: 10px;
-        }
-        .styler {
-            --form-gap-width: 0px !important;
-        }
-        /* Progress Bar Styling */
-        .progress-container {
-            width: 100%;
-            height: 20px;
-            background-color: #e0e0e0;
-            border-radius: 10px;
-            overflow: hidden;
-            margin-bottom: 20px;
-        }
-        .progress-bar {
-            height: 100%;
-            background-color: #4f46e5;
-            transition: width 0.3s ease-in-out;
-            width: calc(var(--current) / var(--total) * 100%);
-        }
-        '''
-
-    def load_initial_loras(self):
-        try:
-            from flux_app.lora import loras as loras_list  # Absolute import
-            self.loras = loras_list
-        except ImportError:
-            print("Warning: lora.py not found, using placeholder LoRAs.")
-            pass
-
-    @spaces.GPU(duration=100)
-    def run_lora(self, prompt, image_input, image_strength, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale, use_enhancer, progress=gr.Progress(track_tqdm=True)):
-        # If prompt enhancer is enabled, generate the enhanced prompt.
-        if use_enhancer:
-            enhanced_prompt = ""
-            # Generate the enhanced prompt (consume the generator to get the final result)
-            for chunk in generate(prompt):
-                enhanced_prompt = chunk
-            prompt_used = enhanced_prompt
-        else:
-            enhanced_prompt = ""
-            prompt_used = prompt
-
-        seed = randomize_seed_if_needed(randomize_seed, seed, MAX_SEED)
-        prompt_mash = prepare_prompt(prompt_used, selected_index, self.loras)
-        selected_lora = self.loras[selected_index]
-
-        unload_lora_weights(self.model_manager.pipe, self.model_manager.pipe_i2i)
-        pipe_to_use = self.model_manager.pipe_i2i if image_input is not None else self.model_manager.pipe
-        load_lora_weights_into_pipeline(pipe_to_use, selected_lora["repo"], selected_lora.get("weights"))
-
-        if image_input is not None:
-            final_image = self.model_manager.generate_image_to_image(
-                prompt_mash, image_input, image_strength, steps, cfg_scale, width, height, lora_scale, seed
-            )
-            yield final_image, seed, gr.update(visible=False), enhanced_prompt
-        else:
-            image_generator = self.model_manager.generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale)
-            final_image = None
-            step_counter = 0
-            for image in image_generator:
-                step_counter += 1
-                final_image = image
-                progress_bar = f'<div class="progress-container"><div class="progress-bar" style="--current: {step_counter}; --total: {steps};"></div></div>'
-                yield image, seed, gr.update(value=progress_bar, visible=True), enhanced_prompt
-
-            yield final_image, seed, gr.update(value=progress_bar, visible=False), enhanced_prompt
-
-    def create_ui(self):
-        with gr.Blocks(theme=gr.themes.Base(), css=self.css, title="Flux LoRA Generation") as app:
-            title = gr.HTML(
-                """<h1>Flux LoRA Generation</h1>""",
-                elem_id="title",
-            )
-            selected_index = gr.State(None)
-
-            with gr.Row():
-                with gr.Column(scale=3):
-                    prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Choose the LoRA and type the prompt")
-                with gr.Column(scale=1, elem_id="gen_column"):
-                    generate_button = 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 self.loras],
-                        label="LoRA Collection",
-                        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/Canopus-LoRA-Flux-Anime")
-                        gr.Markdown("[Check the list of FLUX LoRA's](https://huggingface.co/models?other=base_model:adapter:black-forest-labs/FLUX.1-dev)", elem_id="lora_list")
-                    custom_lora_info = gr.HTML(visible=False)
-                    custom_lora_button = gr.Button("Remove custom LoRA", visible=False)
-                with gr.Column():
-                    progress_bar = gr.Markdown(elem_id="progress", visible=False)
-                    result = gr.Image(label="Generated Image")
-
-            with gr.Row():
-                with gr.Accordion("Advanced Settings", open=False):
-                    with gr.Row():
-                        input_image = gr.Image(label="Input image", type="filepath")
-                        image_strength = gr.Slider(label="Denoise Strength", info="Lower means more image influence", minimum=0.1, maximum=1.0, step=0.01, value=0.75)
-                    with gr.Column():
-                        with gr.Row():
-                            cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=3.5)
-                            steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=28)
-                        with gr.Row():
-                            width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=1024)
-                            height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1024)
-                        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=0.95)
-                    # Prompt Enhancer Section
-                    with gr.Group():
-                        use_enhancer = gr.Checkbox(label="Use Prompt Enhancer", value=True)
-                        show_enhanced_prompt = gr.Checkbox(label="Display Enhanced Prompt", value=False)
-                        enhanced_prompt_box = gr.Textbox(label="Enhanced Prompt", lines=3, visible=False)
-
-            gallery.select(
-                update_selection,
-                inputs=[width, height, gr.State(self.loras)],
-                outputs=[prompt, selected_info, selected_index, width, height]
-            )
-            custom_lora.input(
-                add_custom_lora,
-                inputs=[custom_lora, gr.State(self.loras)],
-                outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, prompt]
-            )
-            custom_lora_button.click(
-                remove_custom_lora,
-                outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, custom_lora]
-            )
-
-            # Toggle the visibility of the enhanced prompt textbox based on the checkbox state.
-            show_enhanced_prompt.change(fn=lambda show: gr.update(visible=show),
-                                        inputs=show_enhanced_prompt,
-                                        outputs=enhanced_prompt_box)
-
-            gr.on(
-                triggers=[generate_button.click, prompt.submit],
-                fn=self.run_lora,
-                inputs=[prompt, input_image, image_strength, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale, use_enhancer],
-                outputs=[result, seed, progress_bar, enhanced_prompt_box]
-            )
-
-            # Credits section added at the bottom
-            with gr.Row():
-                gr.HTML("<div style='text-align:center; font-size:0.9em; margin-top:20px;'>Credits: <a href='https://ruslanmv.com' target='_blank'>ruslanmv.com</a></div>")
-            
-            return app
-
-if __name__ == "__main__":
-    model_manager = ModelManager()
-    frontend = Frontend(model_manager)
-    app = frontend.create_ui()
-    app.queue()
-    app.launch(debug=True)

flux_app/frontend_v1.py

@@ -1,216 +0,0 @@
-# frontend.py
-import gradio as gr
-import sys
-import os
-
-# Add the parent directory to sys.path
-parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
-sys.path.insert(0, parent_dir)
-#print(sys.path) #DEBUG
-
-from flux_app.backend import ModelManager  # Absolute import
-from flux_app.config import MAX_SEED      # Absolute import
-from flux_app.lora_handling import (
-    add_custom_lora, remove_custom_lora, prepare_prompt,
-    unload_lora_weights, load_lora_weights_into_pipeline, update_selection
-)
-from flux_app.utilities import randomize_seed_if_needed, calculateDuration  # Absolute import
-import spaces
-
-
-# Dummy loras data for initial UI setup.
-initial_loras = [
-    {"image": "placeholder.jpg", "title": "Placeholder LoRA", "repo": "placeholder/repo", "weights": None, "trigger_word": ""},
-]
-
-class Frontend:
-    def __init__(self, model_manager: ModelManager):
-        self.model_manager = model_manager
-        self.loras = initial_loras
-        self.load_initial_loras()
-        self.css = self.define_css()
-
-    def define_css(self):
-        # A cleaner, professional CSS styling.
-        return '''
-        /* Title Styling */
-        #title {
-            text-align: center;
-            margin-bottom: 20px;
-        }
-        #title h1 {
-            font-size: 2.5rem;
-            margin: 0;
-            color: #333;
-        }
-        /* Button and Column Styling */
-        #gen_btn {
-            width: 100%;
-            padding: 12px;
-            font-weight: bold;
-            border-radius: 5px;
-        }
-        #gen_column {
-            display: flex;
-            align-items: center;
-            justify-content: center;
-        }
-        /* Gallery and List Styling */
-        #gallery .grid-wrap {
-            margin-top: 15px;
-        }
-        #lora_list {
-            background-color: #f5f5f5;
-            padding: 10px;
-            border-radius: 4px;
-            font-size: 0.9rem;
-        }
-        .card_internal {
-            display: flex;
-            align-items: center;
-            height: 100px;
-            margin-top: 10px;
-        }
-        .card_internal img {
-            margin-right: 10px;
-        }
-        .styler {
-            --form-gap-width: 0px !important;
-        }
-        /* Progress Bar Styling */
-        .progress-container {
-            width: 100%;
-            height: 20px;
-            background-color: #e0e0e0;
-            border-radius: 10px;
-            overflow: hidden;
-            margin-bottom: 20px;
-        }
-        .progress-bar {
-            height: 100%;
-            background-color: #4f46e5;
-            transition: width 0.3s ease-in-out;
-            width: calc(var(--current) / var(--total) * 100%);
-        }
-        '''
-
-    def load_initial_loras(self):
-        try:
-            from flux_app.lora import loras as loras_list  # Absolute import
-            self.loras = loras_list
-        except ImportError:
-            print("Warning: lora.py not found, using placeholder LoRAs.")
-            pass
-
-    @spaces.GPU(duration=100)
-    def run_lora(self, prompt, image_input, image_strength, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)):
-        seed = randomize_seed_if_needed(randomize_seed, seed, MAX_SEED)
-        prompt_mash = prepare_prompt(prompt, selected_index, self.loras)
-        selected_lora = self.loras[selected_index]
-
-        unload_lora_weights(self.model_manager.pipe, self.model_manager.pipe_i2i)
-        pipe_to_use = self.model_manager.pipe_i2i if image_input is not None else self.model_manager.pipe
-        load_lora_weights_into_pipeline(pipe_to_use, selected_lora["repo"], selected_lora.get("weights"))
-
-        if image_input is not None:
-            final_image = self.model_manager.generate_image_to_image(
-                prompt_mash, image_input, image_strength, steps, cfg_scale, width, height, lora_scale, seed
-            )
-            yield final_image, seed, gr.update(visible=False)
-        else:
-             image_generator = self.model_manager.generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale)
-             final_image = None
-             step_counter = 0
-             for image in image_generator:
-                step_counter += 1
-                final_image = image
-                progress_bar = f'<div class="progress-container"><div class="progress-bar" style="--current: {step_counter}; --total: {steps};"></div></div>'
-                yield image, seed, gr.update(value=progress_bar, visible=True)
-
-             yield final_image, seed, gr.update(value=progress_bar, visible=False)
-
-    def create_ui(self):
-        # Using a base theme for a clean and professional look.
-        with gr.Blocks(theme=gr.themes.Base(), css=self.css, title="Flux LoRA Generation") as app:
-            title = gr.HTML(
-                """<h1>Flux LoRA Generation</h1>""",
-                elem_id="title",
-            )
-            selected_index = gr.State(None)
-
-            with gr.Row():
-                with gr.Column(scale=3):
-                    prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Choose the LoRA and type the prompt")
-                with gr.Column(scale=1, elem_id="gen_column"):
-                    generate_button = 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 self.loras],
-                        label="LoRA Collection",
-                        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/Canopus-LoRA-Flux-Anime")
-                        gr.Markdown("[Check the list of FLUX LoRA's](https://huggingface.co/models?other=base_model:adapter:black-forest-labs/FLUX.1-dev)", elem_id="lora_list")
-                    custom_lora_info = gr.HTML(visible=False)
-                    custom_lora_button = gr.Button("Remove custom LoRA", visible=False)
-                with gr.Column():
-                    progress_bar = gr.Markdown(elem_id="progress", visible=False)
-                    result = gr.Image(label="Generated Image")
-
-            with gr.Row():
-                with gr.Accordion("Advanced Settings", open=False):
-                    with gr.Row():
-                        input_image = gr.Image(label="Input image", type="filepath")
-                        image_strength = gr.Slider(label="Denoise Strength", info="Lower means more image influence", minimum=0.1, maximum=1.0, step=0.01, value=0.75)
-                    with gr.Column():
-                        with gr.Row():
-                            cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=3.5)
-                            steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=28)
-                        with gr.Row():
-                            width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=1024)
-                            height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1024)
-                        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=0.95)
-
-            gallery.select(
-                update_selection,
-                inputs=[width, height, gr.State(self.loras)],
-                outputs=[prompt, selected_info, selected_index, width, height]
-            )
-            custom_lora.input(
-                add_custom_lora,
-                inputs=[custom_lora, gr.State(self.loras)],
-                outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, prompt]
-            )
-            custom_lora_button.click(
-                remove_custom_lora,
-                outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, custom_lora]
-            )
-
-            gr.on(
-                triggers=[generate_button.click, prompt.submit],
-                fn=self.run_lora,
-                inputs=[prompt, input_image, image_strength, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale],
-                outputs=[result, seed, progress_bar]
-            )
-
-            # Credits section added at the bottom
-            with gr.Row():
-                gr.HTML("<div style='text-align:center; font-size:0.9em; margin-top:20px;'>Credits: <a href='https://ruslanmv.com' target='_blank'>ruslanmv.com</a></div>")
-            
-            return app
-
-if __name__ == "__main__":
-    model_manager = ModelManager()
-    frontend = Frontend(model_manager)
-    app = frontend.create_ui()
-    app.queue()
-    app.launch()