Add text, audio, and video tabs

This commit adds three new files: text_tab.py, audio_tab.py, and video_tab.py. These files contain the initial implementation of the text, audio, and video tabs for the application. Each tab consists of a simple label that says "Coming soon...". This is the first step towards implementing these tabs in the application.

app.py

@@ -1,816 +1,32 @@
-# Testing one file gradio app for zero gpu spaces not working as expected.
-# Check here for the issue: https://huggingface.co/spaces/zero-gpu-explorers/README/discussions/106#66e278a396acd45223e0d00b
-
-import os
-import gc
-import json
-import random
-from typing import List, Optional
-
-import spaces
 import gradio as gr
-from huggingface_hub import ModelCard
-import torch
-from pydantic import BaseModel
-from PIL import Image
-from diffusers import (
-    AutoPipelineForText2Image,
-    AutoPipelineForImage2Image,
-    AutoPipelineForInpainting,
-    DiffusionPipeline,
-    AutoencoderKL,
-    FluxControlNetModel,
-    FluxMultiControlNetModel,
-)
-from huggingface_hub import hf_hub_download
-from diffusers.schedulers import *
-from huggingface_hub import hf_hub_download
-from controlnet_aux.processor import Processor
-from sd_embed.embedding_funcs import get_weighted_text_embeddings_flux1
-
-
-# Initialize System
-os.system("pip install --upgrade pip")
-
-
-def load_sd():
-    # device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-
-    # Models
-    models = [
-        {
-            "repo_id": "black-forest-labs/FLUX.1-dev",
-            "loader": "flux",
-            "compute_type": torch.bfloat16,
-        }
-    ]
-
-    for model in models:
-        try:
-            model["pipeline"] = AutoPipelineForText2Image.from_pretrained(
-                model['repo_id'],
-                vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16).to(device),
-                torch_dtype = model['compute_type'],
-                safety_checker = None,
-                variant = "fp16"
-            ).to(device)
-        except:
-            model["pipeline"] = AutoPipelineForText2Image.from_pretrained(
-                model['repo_id'],
-                vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=torch.bfloat16).to(device),
-                torch_dtype = model['compute_type'],
-                safety_checker = None
-            ).to(device)
-        
-        model["pipeline"].enable_model_cpu_offload()
-
-
-    # VAE n Refiner
-    flux_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=torch.bfloat16).to(device)
-    sdxl_vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16).to(device)
-    refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", vae=sdxl_vae, torch_dtype=torch.float16, use_safetensors=True, variant="fp16").to(device)
-    refiner.enable_model_cpu_offload()
-
-
-    # ControlNet
-    controlnet = FluxMultiControlNetModel([FluxControlNetModel.from_pretrained(
-        "Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro",
-        torch_dtype=torch.bfloat16
-    ).to(device)])
-    
-    return device, models, flux_vae, sdxl_vae, refiner, controlnet
-
-
-device, models, flux_vae, sdxl_vae, refiner, controlnet = load_sd()
-
-
-# Models
-class ControlNetReq(BaseModel):
-    controlnets: List[str] # ["canny", "tile", "depth"]
-    control_images: List[Image.Image]
-    controlnet_conditioning_scale: List[float]
-    
-    class Config:
-        arbitrary_types_allowed=True
-
-
-class FluxReq(BaseModel):
-    model: str = ""
-    prompt: str = ""
-    fast_generation: Optional[bool] = True
-    loras: Optional[list] = []
-    resize_mode: Optional[str] = "resize_and_fill" # resize_only, crop_and_resize, resize_and_fill
-    scheduler: Optional[str] = "euler_fl"
-    height: int = 1024
-    width: int = 1024
-    num_images_per_prompt: int = 1
-    num_inference_steps: int = 8
-    guidance_scale: float = 3.5
-    seed: Optional[int] = 0
-    refiner: bool = False
-    vae: bool = True
-    controlnet_config: Optional[ControlNetReq] = None
-    
-    class Config:
-        arbitrary_types_allowed=True
-
-
-class FluxImg2ImgReq(FluxReq):
-    image: Image.Image
-    strength: float = 1.0
-    
-    class Config:
-        arbitrary_types_allowed=True
-
-
-class FluxInpaintReq(FluxImg2ImgReq):
-    mask_image: Image.Image
-    
-    class Config:
-        arbitrary_types_allowed=True
-
-
-# Helper Functions
-def get_control_mode(controlnet_config: ControlNetReq):
-    control_mode = []
-    layers = ["canny", "tile", "depth", "blur", "pose", "gray", "low_quality"]
-    
-    for c in controlnet_config.controlnets:
-        if c in layers:
-            control_mode.append(layers.index(c))
-    
-    return control_mode
-
-
-def get_pipe(request: FluxReq | FluxImg2ImgReq | FluxInpaintReq):
-    for m in models:
-        if m['repo_id'] == request.model:
-            pipe_args = {
-                "pipeline": m['pipeline'],
-            }
-            
-            
-            # Set ControlNet config
-            if request.controlnet_config:
-                pipe_args["control_mode"] = get_control_mode(request.controlnet_config)
-                pipe_args["controlnet"] = [controlnet]
-            
-            
-            # Choose Pipeline Mode
-            if isinstance(request, FluxReq):
-                pipe_args['pipeline'] = AutoPipelineForText2Image.from_pipe(**pipe_args)
-            elif isinstance(request, FluxImg2ImgReq):
-                pipe_args['pipeline'] = AutoPipelineForImage2Image.from_pipe(**pipe_args)
-            elif isinstance(request, FluxInpaintReq):
-                pipe_args['pipeline'] = AutoPipelineForInpainting.from_pipe(**pipe_args)
-            
-            
-            # Enable or Disable Refiner
-            if request.vae:
-                pipe_args["pipeline"].vae = flux_vae
-            elif not request.vae:
-                pipe_args["pipeline"].vae = None
-            
-            
-            # Set Scheduler
-            pipe_args["pipeline"].scheduler = FlowMatchEulerDiscreteScheduler.from_config(pipe_args["pipeline"].scheduler.config)
-            
-            
-            # Set Loras
-            if request.loras:
-                for i, lora in enumerate(request.loras):
-                    pipe_args["pipeline"].load_lora_weights(request.lora['repo_id'], adapter_name=f"lora_{i}")
-                adapter_names = [f"lora_{i}" for i in range(len(request.loras))]
-                adapter_weights = [lora['weight'] for lora in request.loras]
-                
-                if request.fast_generation:
-                    hyper_lora = hf_hub_download("ByteDance/Hyper-SD", "Hyper-FLUX.1-dev-8steps-lora.safetensors")
-                    hyper_weight = 0.125
-                    pipe_args["pipeline"].load_lora_weights(hyper_lora, adapter_name="hyper_lora")
-                    adapter_names.append("hyper_lora")
-                    adapter_weights.append(hyper_weight)
-                
-                pipe_args["pipeline"].set_adapters(adapter_names, adapter_weights)
-            
-            return pipe_args
-
-
-def resize_images(images: List[Image.Image], height: int, width: int, resize_mode: str):
-    for image in images:
-        if resize_mode == "resize_only":
-            image = image.resize((width, height))
-        elif resize_mode == "crop_and_resize":
-            image = image.crop((0, 0, width, height))
-        elif resize_mode == "resize_and_fill":
-            image = image.resize((width, height), Image.Resampling.LANCZOS)
-
-    return images
-
-
-def get_controlnet_images(controlnet_config: ControlNetReq, height: int, width: int, resize_mode: str):
-    response_images = []
-    control_images = resize_images(controlnet_config.control_images, height, width, resize_mode)
-    for controlnet, image in zip(controlnet_config.controlnets, control_images):
-        if controlnet == "canny":
-            processor = Processor('canny')
-        elif controlnet == "depth":
-            processor = Processor('depth_midas')
-        elif controlnet == "pose":
-            processor = Processor('openpose_full')
-        else:
-            raise ValueError(f"Invalid Controlnet: {controlnet}")
-    
-        response_images.append(processor(image, to_pil=True))
-    
-    return response_images
-
-
-def get_prompt_attention(pipeline, prompt):
-    return get_weighted_text_embeddings_flux1(pipeline, prompt)
-
-
-def cleanup(pipeline, loras = None):
-    if loras:
-        pipeline.unload_lora_weights()
-    gc.collect()
-    torch.cuda.empty_cache()
-
 
-# Gen Function
-def gen_img(request: FluxReq | FluxImg2ImgReq | FluxInpaintReq):
-    pipe_args = get_pipe(request)
-    pipeline = pipe_args["pipeline"]
-    try:
-        positive_prompt_embeds, positive_prompt_pooled = get_prompt_attention(pipeline, request.prompt)
-        
-        # Common Args
-        args = {
-            'prompt_embeds': positive_prompt_embeds,
-            'pooled_prompt_embeds': positive_prompt_pooled,
-            'height': request.height,
-            'width': request.width,
-            'num_images_per_prompt': request.num_images_per_prompt,
-            'num_inference_steps': request.num_inference_steps,
-            'guidance_scale': request.guidance_scale,
-            'generator': [torch.Generator(device=device).manual_seed(request.seed + i) if not request.seed is any([None, 0, -1]) else torch.Generator(device=device).manual_seed(random.randint(0, 2**32 - 1)) for i in range(request.num_images_per_prompt)],
-        }
-        
-        if request.controlnet_config:
-            args['control_mode'] = get_control_mode(request.controlnet_config)
-            args['control_images'] = get_controlnet_images(request.controlnet_config, request.height, request.width, request.resize_mode)
-            args['controlnet_conditioning_scale'] = request.controlnet_config.controlnet_conditioning_scale
-        
-        if isinstance(request, (FluxImg2ImgReq, FluxInpaintReq)):
-            args['image'] = resize_images([request.image], request.height, request.width, request.resize_mode)[0]
-            args['strength'] = request.strength
-        
-        if isinstance(request, FluxInpaintReq):
-            args['mask_image'] = resize_images([request.mask_image], request.height, request.width, request.resize_mode)[0]
-        
-        # Generate
-        images = pipeline(**args).images
-        
-        # Refiner
-        if request.refiner:
-            images = refiner(image=images, prompt=request.prompt, num_inference_steps=40, denoising_start=0.7).images
-        
-        cleanup(pipeline, request.loras)
-        
-        return images
-    except Exception as e:
-        cleanup(pipeline, request.loras)
-        raise gr.Error(f"Error: {e}")
-
-
-
-# CSS
-css = """
-@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;600&display=swap');
-body {
-    font-family: 'Poppins', sans-serif !important;
-}
-.center-content {
-    text-align: center;
-    max-width: 600px;
-    margin: 0 auto;
-    padding: 20px;
-}
-.center-content h1 {
-    font-weight: 600;
-    margin-bottom: 1rem;
-}
-.center-content p {
-    margin-bottom: 1.5rem;
-}
-"""
-
-
-flux_models = ["black-forest-labs/FLUX.1-dev"]
-with open("data/images/loras/flux.json", "r") as f:
-    loras = json.load(f)
-
-
-# Event functions
-def update_fast_generation(model, fast_generation):
-    if fast_generation:
-        return (
-            gr.update(
-                value=3.5
-            ),
-            gr.update(
-                value=8
-            )
-        )
-
-
-def selected_lora_from_gallery(evt: gr.SelectData):
-    return (
-        gr.update(
-            value=evt.index
-        )
-    )
-
-
-def update_selected_lora(custom_lora):
-    link = custom_lora.split("/")
-    
-    if len(link) == 2:
-        model_card = ModelCard.load(custom_lora)
-        trigger_word = model_card.data.get("instance_prompt", "")
-        image_url = f"""https://huggingface.co/{custom_lora}/resolve/main/{model_card.data.get("widget", [{}])[0].get("output", {}).get("url", None)}"""
-        
-        custom_lora_info_css = """
-        <style>
-            .custom-lora-info {
-                font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Oxygen', 'Ubuntu', 'Cantarell', 'Fira Sans', 'Droid Sans', 'Helvetica Neue', sans-serif;
-                background: linear-gradient(135deg, #4a90e2, #7b61ff);
-                color: white;
-                padding: 16px;
-                border-radius: 8px;
-                box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
-                margin: 16px 0;
-            }
-            .custom-lora-header {
-                font-size: 18px;
-                font-weight: 600;
-                margin-bottom: 12px;
-            }
-            .custom-lora-content {
-                display: flex;
-                align-items: center;
-                background-color: rgba(255, 255, 255, 0.1);
-                border-radius: 6px;
-                padding: 12px;
-            }
-            .custom-lora-image {
-                width: 80px;
-                height: 80px;
-                object-fit: cover;
-                border-radius: 6px;
-                margin-right: 16px;
-            }
-            .custom-lora-text h3 {
-                margin: 0 0 8px 0;
-                font-size: 16px;
-                font-weight: 600;
-            }
-            .custom-lora-text small {
-                font-size: 14px;
-                opacity: 0.9;
-            }
-            .custom-trigger-word {
-                background-color: rgba(255, 255, 255, 0.2);
-                padding: 2px 6px;
-                border-radius: 4px;
-                font-weight: 600;
-            }
-        </style>
-        """
-
-        custom_lora_info_html = f"""
-        <div class="custom-lora-info">
-            <div class="custom-lora-header">Custom LoRA: {custom_lora}</div>
-            <div class="custom-lora-content">
-                <img class="custom-lora-image" src="{image_url}" alt="LoRA preview">
-                <div class="custom-lora-text">
-                    <h3>{link[1].replace("-", " ").replace("_", " ")}</h3>
-                    <small>{"Using: <span class='custom-trigger-word'>"+trigger_word+"</span> as the trigger word" if trigger_word else "No trigger word found. If there's a trigger word, include it in your prompt"}</small>
-                </div>
-            </div>
-        </div>
-        """
-
-        custom_lora_info_html = f"{custom_lora_info_css}{custom_lora_info_html}"
-
-        return (
-            gr.update( # selected_lora
-                value=custom_lora,
-            ),
-            gr.update( # custom_lora_info
-                value=custom_lora_info_html,
-                visible=True
-            )
-        )
-
-    else:
-        return (
-            gr.update( # selected_lora
-                value=custom_lora,
-            ),
-            gr.update( # custom_lora_info
-                value=custom_lora_info_html if len(link) == 0 else "",
-                visible=False
-            )
-        )
-
-
-def add_to_enabled_loras(model, selected_lora, enabled_loras):
-    lora_data = loras
-    try:
-        selected_lora = int(selected_lora)
-        
-        if 0 <= selected_lora: # is the index of the lora in the gallery
-            lora_info = lora_data[selected_lora]
-            enabled_loras.append({
-                "repo_id": lora_info["repo"],
-                "trigger_word": lora_info["trigger_word"]
-            })
-    except ValueError:
-        link = selected_lora.split("/")
-        if len(link) == 2:
-            model_card = ModelCard.load(selected_lora)
-            trigger_word = model_card.data.get("instance_prompt", "")
-            enabled_loras.append({
-                "repo_id": selected_lora,
-                "trigger_word": trigger_word
-            })
-    
-    return (
-        gr.update( # selected_lora
-            value=""
-        ),
-        gr.update( # custom_lora_info
-            value="",
-            visible=False
-        ),
-        gr.update( # enabled_loras
-            value=enabled_loras
-        )
+from config import css
+from tabs.image_tab import image_tab
+from tabs.audio_tab import audio_tab
+from tabs.video_tab import video_tab
+from tabs.text_tab import text_tab
+
+def main():
+    with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
+        # Header
+        with gr.Column(elem_classes="center-content"):
+            gr.Markdown("""
+            # 🚀 AAI: All AI
+            Unleash your creativity with our multi-modal AI platform.
+            [![Sync code to HF Space](https://github.com/mantrakp04/aai/actions/workflows/hf-space.yml/badge.svg)](https://github.com/mantrakp04/aai/actions/workflows/hf-space.yml)
+            """)
+
+        # Tabs
+        with gr.Tabs():
+            image_tab()
+            audio_tab()
+            video_tab()
+            text_tab()
+
+    demo.launch(
+        share=False,
+        debug=True,
     )
 
-
-def update_lora_sliders(enabled_loras):
-    sliders = []
-    remove_buttons = []
-    
-    for lora in enabled_loras:
-        sliders.append(
-            gr.update(
-                label=lora.get("repo_id", ""),
-                info=f"Trigger Word: {lora.get('trigger_word', '')}",
-                visible=True,
-                interactive=True
-            )
-        )
-        remove_buttons.append(
-            gr.update(
-                visible=True,
-                interactive=True
-            )
-        )
-    
-    if len(sliders) < 6:
-        for i in range(len(sliders), 6):
-            sliders.append(
-                gr.update(
-                    visible=False
-                )
-            )
-            remove_buttons.append(
-                gr.update(
-                    visible=False
-                )
-            )
-    
-    return *sliders, *remove_buttons
-
-
-def remove_from_enabled_loras(enabled_loras, index):
-    enabled_loras.pop(index)
-    return (
-        gr.update(
-            value=enabled_loras
-        )
-    )
-
-
-@spaces.GPU
-def generate_image(
-        model, prompt, fast_generation, enabled_loras,
-        lora_slider_0, lora_slider_1, lora_slider_2, lora_slider_3, lora_slider_4, lora_slider_5,
-        img2img_image, inpaint_image, canny_image, pose_image, depth_image,
-        img2img_strength, inpaint_strength, canny_strength, pose_strength, depth_strength,
-        resize_mode,
-        scheduler, image_height, image_width, image_num_images_per_prompt,
-        image_num_inference_steps, image_guidance_scale, image_seed,
-        refiner, vae
-    ):
-        base_args = {
-            "model": model,
-            "prompt": prompt,
-            "fast_generation": fast_generation,
-            "loras": None,
-            "resize_mode": resize_mode,
-            "scheduler": scheduler,
-            "height": int(image_height),
-            "width": int(image_width),
-            "num_images_per_prompt": float(image_num_images_per_prompt),
-            "num_inference_steps": float(image_num_inference_steps),
-            "guidance_scale": float(image_guidance_scale),
-            "seed": int(image_seed),
-            "refiner": refiner,
-            "vae": vae,
-            "controlnet_config": None,
-        }
-        base_args = FluxReq(**base_args)
-        
-        if len(enabled_loras) > 0:
-            base_args.loras = []
-            for enabled_lora, slider in zip(enabled_loras, [lora_slider_0, lora_slider_1, lora_slider_2, lora_slider_3, lora_slider_4, lora_slider_5]):
-                if enabled_lora['repo_id']:
-                    base_args.loras.append({
-                        "repo_id": enabled_lora['repo_id'],
-                        "weight": slider
-                    })
-        
-        image = None
-        mask_image = None
-        strength = None
-        
-        if img2img_image:
-            image = img2img_image
-            strength = float(img2img_strength)
-            
-            base_args = FluxImg2ImgReq(
-                **base_args.__dict__,
-                image=image,
-                strength=strength
-            )
-        elif inpaint_image:
-            image = inpaint_image['background'] if not all(pixel == (0, 0, 0) for pixel in list(inpaint_image['background'].getdata())) else None
-            mask_image = inpaint_image['layers'][0] if image else None
-            strength = float(inpaint_strength)
-            
-            if image and mask_image:
-                base_args = FluxInpaintReq(
-                    **base_args.__dict__,
-                    image=image,
-                    mask_image=mask_image,
-                    strength=strength
-                )
-        elif any([canny_image, pose_image, depth_image]):
-            base_args.controlnet_config = ControlNetReq(
-                controlnets=[],
-                control_images=[],
-                controlnet_conditioning_scale=[]
-            )
-            
-            if canny_image:
-                base_args.controlnet_config.controlnets.append("canny")
-                base_args.controlnet_config.control_images.append(canny_image)
-                base_args.controlnet_config.controlnet_conditioning_scale.append(float(canny_strength))
-            if pose_image:
-                base_args.controlnet_config.controlnets.append("pose")
-                base_args.controlnet_config.control_images.append(pose_image)
-                base_args.controlnet_config.controlnet_conditioning_scale.append(float(pose_strength))
-            if depth_image:
-                base_args.controlnet_config.controlnets.append("depth")
-                base_args.controlnet_config.control_images.append(depth_image)
-                base_args.controlnet_config.controlnet_conditioning_scale.append(float(depth_strength))
-        else:
-            base_args = FluxReq(**base_args.__dict__)
-        
-        return gr.update(
-            value=gen_img(base_args),
-            interactive=True
-        )
-
-
-# Main Gradio app
-with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
-    # Header
-    with gr.Column(elem_classes="center-content"):
-        gr.Markdown("""
-        # 🚀 AAI: All AI
-        Unleash your creativity with our multi-modal AI platform.
-        [![Sync code to HF Space](https://github.com/mantrakp04/aai/actions/workflows/hf-space.yml/badge.svg)](https://github.com/mantrakp04/aai/actions/workflows/hf-space.yml)
-        """)
-
-    # Tabs
-    with gr.Tabs():
-        with gr.Tab(label="🖼️ Image"):
-            with gr.Tabs():
-                with gr.Tab("Flux"):
-                    """
-                    Create the image tab for Generative Image Generation Models
-                    
-                    Args:
-                    models: list
-                        A list containing the models repository paths
-                    gap_iol, gap_la, gap_le, gap_eio, gap_io: Optional[List[dict]]
-                        A list of dictionaries containing the title and component for the custom gradio component
-                        Example:
-                        def gr_comp():
-                            gr.Label("Hello World")
-                        
-                        [
-                            {
-                                'title': "Title",
-                                'component': gr_comp()
-                            }
-                        ]
-                    loras: list
-                        A list of dictionaries containing the image and title for the Loras Gallery
-                        Generally a loaded json file from the data folder
-                    
-                    """
-                    def process_gaps(gaps: List[dict]):
-                        for gap in gaps:
-                            with gr.Accordion(gap['title']):
-                                gap['component']
-                    
-                    
-                    with gr.Row():
-                        with gr.Column():
-                            with gr.Group() as image_options:
-                                model = gr.Dropdown(label="Models", choices=flux_models, value=flux_models[0], interactive=True)
-                                prompt = gr.Textbox(lines=5, label="Prompt")
-                                fast_generation = gr.Checkbox(label="Fast Generation (Hyper-SD) 🧪")
-                            
-                            
-                            with gr.Accordion("Loras", open=True): # Lora Gallery
-                                lora_gallery = gr.Gallery(
-                                    label="Gallery",
-                                    value=[(lora['image'], lora['title']) for lora in loras],
-                                    allow_preview=False,
-                                    columns=3,
-                                    rows=3,
-                                    type="pil"
-                                )
-                                
-                                with gr.Group():
-                                    with gr.Column():
-                                        with gr.Row():
-                                            custom_lora = gr.Textbox(label="Custom Lora", info="Enter a Huggingface repo path")
-                                            selected_lora = gr.Textbox(label="Selected Lora", info="Choose from the gallery or enter a custom LoRA")
-                                        
-                                        custom_lora_info = gr.HTML(visible=False)
-                                        add_lora = gr.Button(value="Add LoRA")
-                                        
-                                        enabled_loras = gr.State(value=[])
-                                        with gr.Group():
-                                            with gr.Row():
-                                                for i in range(6): # only support max 6 loras due to inference time
-                                                    with gr.Column():
-                                                        with gr.Column(scale=2):
-                                                            globals()[f"lora_slider_{i}"] = gr.Slider(label=f"LoRA {i+1}", minimum=0, maximum=1, step=0.01, value=0.8, visible=False, interactive=True)
-                                                        with gr.Column():
-                                                            globals()[f"lora_remove_{i}"] = gr.Button(value="Remove LoRA", visible=False)
-
-                            
-                            with gr.Accordion("Embeddings", open=False): # Embeddings
-                                gr.Label("To be implemented")
-                            
-                            
-                            with gr.Accordion("Image Options"): # Image Options
-                                with gr.Tabs():
-                                    image_options = {
-                                        "img2img": "Upload Image",
-                                        "inpaint": "Upload Image",
-                                        "canny": "Upload Image",
-                                        "pose": "Upload Image",
-                                        "depth": "Upload Image",
-                                    }
-                                    
-                                    for image_option, label in image_options.items():
-                                        with gr.Tab(image_option):
-                                            if not image_option in ['inpaint', 'scribble']:
-                                                globals()[f"{image_option}_image"] = gr.Image(label=label, type="pil")
-                                            elif image_option in ['inpaint', 'scribble']:
-                                                globals()[f"{image_option}_image"] = gr.ImageEditor(
-                                                    label=label,
-                                                    image_mode='RGB',
-                                                    layers=False,
-                                                    brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed") if image_option == 'inpaint' else gr.Brush(),
-                                                    interactive=True,
-                                                    type="pil",
-                                                )
-                                            
-                                            # Image Strength (Co-relates to controlnet strength, strength for img2img n inpaint)
-                                            globals()[f"{image_option}_strength"] = gr.Slider(label="Strength", minimum=0, maximum=1, step=0.01, value=1.0, interactive=True)
-                                    
-                                    resize_mode = gr.Radio(
-                                        label="Resize Mode",
-                                        choices=["crop and resize", "resize only", "resize and fill"],
-                                        value="resize and fill",
-                                        interactive=True
-                                    )
-                        
-                        
-                        with gr.Column():
-                            with gr.Group():
-                                output_images = gr.Gallery(
-                                        label="Output Images",
-                                        value=[],
-                                        allow_preview=True,
-                                        type="pil",
-                                        interactive=False,
-                                    )
-                                generate_images = gr.Button(value="Generate Images", variant="primary")            
-                            
-                            with gr.Accordion("Advance Settings", open=True):
-                                with gr.Row():
-                                    scheduler = gr.Dropdown(
-                                        label="Scheduler",
-                                        choices = [
-                                            "fm_euler"
-                                        ],
-                                        value="fm_euler",
-                                        interactive=True
-                                    )
-
-                                with gr.Row():
-                                    for column in range(2):
-                                        with gr.Column():
-                                            options = [
-                                                ("Height", "image_height", 64, 1024, 64, 1024, True),
-                                                ("Width", "image_width", 64, 1024, 64, 1024, True),
-                                                ("Num Images Per Prompt", "image_num_images_per_prompt", 1, 4, 1, 1, True),
-                                                ("Num Inference Steps", "image_num_inference_steps", 1, 100, 1, 20, True),
-                                                ("Clip Skip", "image_clip_skip", 0, 2, 1, 2, False),
-                                                ("Guidance Scale", "image_guidance_scale", 0, 20, 0.5, 3.5, True),
-                                                ("Seed", "image_seed", 0, 100000, 1, random.randint(0, 100000), True),
-                                            ]
-                                            for label, var_name, min_val, max_val, step, value, visible in options[column::2]:
-                                                globals()[var_name] = gr.Slider(label=label, minimum=min_val, maximum=max_val, step=step, value=value, visible=visible, interactive=True)
-                                
-                                with gr.Row():
-                                    refiner = gr.Checkbox(
-                                        label="Refiner 🧪",
-                                        value=False,
-                                    )
-                                    vae = gr.Checkbox(
-                                        label="VAE",
-                                        value=True,
-                                    )
-
-
-                    # Events
-                    # Base Options
-                    fast_generation.change(update_fast_generation, [model, fast_generation], [image_guidance_scale, image_num_inference_steps]) # Fast Generation # type: ignore
-                    
-
-                    # Lora Gallery
-                    lora_gallery.select(selected_lora_from_gallery, None, selected_lora)
-                    custom_lora.change(update_selected_lora, custom_lora, [custom_lora, selected_lora])
-                    add_lora.click(add_to_enabled_loras, [model, selected_lora, enabled_loras], [selected_lora, custom_lora_info, enabled_loras])
-                    enabled_loras.change(update_lora_sliders, enabled_loras, [lora_slider_0, lora_slider_1, lora_slider_2, lora_slider_3, lora_slider_4, lora_slider_5, lora_remove_0, lora_remove_1, lora_remove_2, lora_remove_3, lora_remove_4, lora_remove_5]) # type: ignore
-
-                    for i in range(6):
-                        globals()[f"lora_remove_{i}"].click(
-                            lambda enabled_loras, index=i: remove_from_enabled_loras(enabled_loras, index),
-                            [enabled_loras],
-                            [enabled_loras]
-                        )
-                    
-
-                    # Generate Image
-                    generate_images.click(
-                        generate_image, # type: ignore
-                        [
-                            model, prompt, fast_generation, enabled_loras,
-                            lora_slider_0, lora_slider_1, lora_slider_2, lora_slider_3, lora_slider_4, lora_slider_5, # type: ignore
-                            img2img_image, inpaint_image, canny_image, pose_image, depth_image, # type: ignore
-                            img2img_strength, inpaint_strength, canny_strength, pose_strength, depth_strength, # type: ignore
-                            resize_mode,
-                            scheduler, image_height, image_width, image_num_images_per_prompt, # type: ignore
-                            image_num_inference_steps, image_guidance_scale, image_seed, # type: ignore
-                            refiner, vae
-                        ],
-                        [output_images]
-                    )
-                with gr.Tab("SDXL"):
-                    gr.Label("To be implemented")
-        with gr.Tab(label="🎵 Audio"):
-            gr.Label("Coming soon!")
-        with gr.Tab(label="🎬 Video"):
-            gr.Label("Coming soon!")
-        with gr.Tab(label="📄 Text"):
-            gr.Label("Coming soon!")
-
-
-demo.launch(
-    share=False,
-    debug=True,
-)
+if __name__ == "__main__":
+    main()

config.py

@@ -0,0 +1,36 @@
+# config.py
+
+import json
+
+css = """
+@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;600&display=swap');
+body {
+    font-family: 'Poppins', sans-serif !important;
+}
+.center-content {
+    text-align: center;
+    max-width: 600px;
+    margin: 0 auto;
+    padding: 20px;
+}
+.center-content h1 {
+    font-weight: 600;
+    margin-bottom: 1rem;
+}
+.center-content p {
+    margin-bottom: 1.5rem;
+}
+"""
+
+
+# Models
+flux_models = ["black-forest-labs/FLUX.1-dev"]
+sdxl_models = ["stabilityai/stable-diffusion-xl-base-1.0"]
+
+
+# Load LoRAs
+with open("data/loras/flux.json", "r") as f:
+    flux_loras = json.load(f)
+
+with open("data/loras/sdxl.json", "r") as f:
+    sdxl_loras = json.load(f)

modules/events/flux_events.py

@@ -0,0 +1,298 @@
+import json
+from typing import List
+
+import spaces
+import gradio as gr
+from huggingface_hub import ModelCard
+
+from modules.helpers.flux_helpers import FluxReq, FluxImg2ImgReq, FluxInpaintReq, ControlNetReq, gen_img
+from config import flux_models, flux_loras
+
+flux_models = ["black-forest-labs/FLUX.1-dev"]
+loras = flux_loras
+
+
+# Event functions
+def update_fast_generation(model, fast_generation):
+    if fast_generation:
+        return (
+            gr.update(
+                value=3.5
+            ),
+            gr.update(
+                value=8
+            )
+        )
+
+
+def selected_lora_from_gallery(evt: gr.SelectData):
+    return (
+        gr.update(
+            value=evt.index
+        )
+    )
+
+
+def update_selected_lora(custom_lora):
+    link = custom_lora.split("/")
+    
+    if len(link) == 2:
+        model_card = ModelCard.load(custom_lora)
+        trigger_word = model_card.data.get("instance_prompt", "")
+        image_url = f"""https://huggingface.co/{custom_lora}/resolve/main/{model_card.data.get("widget", [{}])[0].get("output", {}).get("url", None)}"""
+        
+        custom_lora_info_css = """
+        <style>
+            .custom-lora-info {
+                font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Oxygen', 'Ubuntu', 'Cantarell', 'Fira Sans', 'Droid Sans', 'Helvetica Neue', sans-serif;
+                background: linear-gradient(135deg, #4a90e2, #7b61ff);
+                color: white;
+                padding: 16px;
+                border-radius: 8px;
+                box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+                margin: 16px 0;
+            }
+            .custom-lora-header {
+                font-size: 18px;
+                font-weight: 600;
+                margin-bottom: 12px;
+            }
+            .custom-lora-content {
+                display: flex;
+                align-items: center;
+                background-color: rgba(255, 255, 255, 0.1);
+                border-radius: 6px;
+                padding: 12px;
+            }
+            .custom-lora-image {
+                width: 80px;
+                height: 80px;
+                object-fit: cover;
+                border-radius: 6px;
+                margin-right: 16px;
+            }
+            .custom-lora-text h3 {
+                margin: 0 0 8px 0;
+                font-size: 16px;
+                font-weight: 600;
+            }
+            .custom-lora-text small {
+                font-size: 14px;
+                opacity: 0.9;
+            }
+            .custom-trigger-word {
+                background-color: rgba(255, 255, 255, 0.2);
+                padding: 2px 6px;
+                border-radius: 4px;
+                font-weight: 600;
+            }
+        </style>
+        """
+
+        custom_lora_info_html = f"""
+        <div class="custom-lora-info">
+            <div class="custom-lora-header">Custom LoRA: {custom_lora}</div>
+            <div class="custom-lora-content">
+                <img class="custom-lora-image" src="{image_url}" alt="LoRA preview">
+                <div class="custom-lora-text">
+                    <h3>{link[1].replace("-", " ").replace("_", " ")}</h3>
+                    <small>{"Using: <span class='custom-trigger-word'>"+trigger_word+"</span> as the trigger word" if trigger_word else "No trigger word found. If there's a trigger word, include it in your prompt"}</small>
+                </div>
+            </div>
+        </div>
+        """
+
+        custom_lora_info_html = f"{custom_lora_info_css}{custom_lora_info_html}"
+
+        return (
+            gr.update( # selected_lora
+                value=custom_lora,
+            ),
+            gr.update( # custom_lora_info
+                value=custom_lora_info_html,
+                visible=True
+            )
+        )
+
+    else:
+        return (
+            gr.update( # selected_lora
+                value=custom_lora,
+            ),
+            gr.update( # custom_lora_info
+                value=custom_lora_info_html if len(link) == 0 else "",
+                visible=False
+            )
+        )
+
+
+def add_to_enabled_loras(model, selected_lora, enabled_loras):
+    lora_data = loras
+    try:
+        selected_lora = int(selected_lora)
+        
+        if 0 <= selected_lora: # is the index of the lora in the gallery
+            lora_info = lora_data[selected_lora]
+            enabled_loras.append({
+                "repo_id": lora_info["repo"],
+                "trigger_word": lora_info["trigger_word"]
+            })
+    except ValueError:
+        link = selected_lora.split("/")
+        if len(link) == 2:
+            model_card = ModelCard.load(selected_lora)
+            trigger_word = model_card.data.get("instance_prompt", "")
+            enabled_loras.append({
+                "repo_id": selected_lora,
+                "trigger_word": trigger_word
+            })
+    
+    return (
+        gr.update( # selected_lora
+            value=""
+        ),
+        gr.update( # custom_lora_info
+            value="",
+            visible=False
+        ),
+        gr.update( # enabled_loras
+            value=enabled_loras
+        )
+    )
+
+
+def update_lora_sliders(enabled_loras):
+    sliders = []
+    remove_buttons = []
+    
+    for lora in enabled_loras:
+        sliders.append(
+            gr.update(
+                label=lora.get("repo_id", ""),
+                info=f"Trigger Word: {lora.get('trigger_word', '')}",
+                visible=True,
+                interactive=True
+            )
+        )
+        remove_buttons.append(
+            gr.update(
+                visible=True,
+                interactive=True
+            )
+        )
+    
+    if len(sliders) < 6:
+        for i in range(len(sliders), 6):
+            sliders.append(
+                gr.update(
+                    visible=False
+                )
+            )
+            remove_buttons.append(
+                gr.update(
+                    visible=False
+                )
+            )
+    
+    return *sliders, *remove_buttons
+
+
+def remove_from_enabled_loras(enabled_loras, index):
+    enabled_loras.pop(index)
+    return (
+        gr.update(
+            value=enabled_loras
+        )
+    )
+
+
+@spaces.GPU
+def generate_image(
+        model, prompt, fast_generation, enabled_loras,
+        lora_slider_0, lora_slider_1, lora_slider_2, lora_slider_3, lora_slider_4, lora_slider_5,
+        img2img_image, inpaint_image, canny_image, pose_image, depth_image,
+        img2img_strength, inpaint_strength, canny_strength, pose_strength, depth_strength,
+        resize_mode,
+        scheduler, image_height, image_width, image_num_images_per_prompt,
+        image_num_inference_steps, image_guidance_scale, image_seed,
+        refiner, vae
+    ):
+        base_args = {
+            "model": model,
+            "prompt": prompt,
+            "fast_generation": fast_generation,
+            "loras": None,
+            "resize_mode": resize_mode,
+            "scheduler": scheduler,
+            "height": int(image_height),
+            "width": int(image_width),
+            "num_images_per_prompt": float(image_num_images_per_prompt),
+            "num_inference_steps": float(image_num_inference_steps),
+            "guidance_scale": float(image_guidance_scale),
+            "seed": int(image_seed),
+            "refiner": refiner,
+            "vae": vae,
+            "controlnet_config": None,
+        }
+        base_args = FluxReq(**base_args)
+        
+        if len(enabled_loras) > 0:
+            base_args.loras = []
+            for enabled_lora, slider in zip(enabled_loras, [lora_slider_0, lora_slider_1, lora_slider_2, lora_slider_3, lora_slider_4, lora_slider_5]):
+                if enabled_lora['repo_id']:
+                    base_args.loras.append({
+                        "repo_id": enabled_lora['repo_id'],
+                        "weight": slider
+                    })
+        
+        image = None
+        mask_image = None
+        strength = None
+        
+        if img2img_image:
+            image = img2img_image
+            strength = float(img2img_strength)
+            
+            base_args = FluxImg2ImgReq(
+                **base_args.__dict__,
+                image=image,
+                strength=strength
+            )
+        elif inpaint_image:
+            image = inpaint_image['background'] if not all(pixel == (0, 0, 0) for pixel in list(inpaint_image['background'].getdata())) else None
+            mask_image = inpaint_image['layers'][0] if image else None
+            strength = float(inpaint_strength)
+            
+            if image and mask_image:
+                base_args = FluxInpaintReq(
+                    **base_args.__dict__,
+                    image=image,
+                    mask_image=mask_image,
+                    strength=strength
+                )
+        elif any([canny_image, pose_image, depth_image]):
+            base_args.controlnet_config = ControlNetReq(
+                controlnets=[],
+                control_images=[],
+                controlnet_conditioning_scale=[]
+            )
+            
+            if canny_image:
+                base_args.controlnet_config.controlnets.append("canny")
+                base_args.controlnet_config.control_images.append(canny_image)
+                base_args.controlnet_config.controlnet_conditioning_scale.append(float(canny_strength))
+            if pose_image:
+                base_args.controlnet_config.controlnets.append("pose")
+                base_args.controlnet_config.control_images.append(pose_image)
+                base_args.controlnet_config.controlnet_conditioning_scale.append(float(pose_strength))
+            if depth_image:
+                base_args.controlnet_config.controlnets.append("depth")
+                base_args.controlnet_config.control_images.append(depth_image)
+                base_args.controlnet_config.controlnet_conditioning_scale.append(float(depth_strength))
+        else:
+            base_args = FluxReq(**base_args.__dict__)
+        
+        return gr.update(
+            value=gen_img(base_args),
+            interactive=True
+        )

modules/helpers/common_helpers.py

@@ -0,0 +1,84 @@
+import gc
+from typing import List, Optional, Dict, Any
+
+import torch
+from pydantic import BaseModel
+from PIL import Image
+from diffusers.schedulers import *
+from controlnet_aux.processor import Processor
+
+from .flux_helpers import ControlNetReq
+
+
+class BaseReq(BaseModel):
+    model: str = ""
+    prompt: str = ""
+    fast_generation: Optional[bool] = True
+    loras: Optional[list] = []
+    resize_mode: Optional[str] = "resize_and_fill" # resize_only, crop_and_resize, resize_and_fill
+    scheduler: Optional[str] = "euler_fl"
+    height: int = 1024
+    width: int = 1024
+    num_images_per_prompt: int = 1
+    num_inference_steps: int = 8
+    guidance_scale: float = 3.5
+    seed: Optional[int] = 0
+    refiner: bool = False
+    vae: bool = True
+    controlnet_config: Optional[ControlNetReq] = None
+    custom_addons: Optional[Dict[Any, Any]] = None
+    
+    class Config:
+        arbitrary_types_allowed=True
+
+
+class BaseImg2ImgReq(BaseReq):
+    image: Image.Image
+    strength: float = 1.0
+    
+    class Config:
+        arbitrary_types_allowed=True
+
+
+class BaseInpaintReq(BaseImg2ImgReq):
+    mask_image: Image.Image
+    
+    class Config:
+        arbitrary_types_allowed=True
+
+
+def resize_images(images: List[Image.Image], height: int, width: int, resize_mode: str):
+    for image in images:
+        if resize_mode == "resize_only":
+            image = image.resize((width, height))
+        elif resize_mode == "crop_and_resize":
+            image = image.crop((0, 0, width, height))
+        elif resize_mode == "resize_and_fill":
+            image = image.resize((width, height), Image.Resampling.LANCZOS)
+
+    return images
+
+
+def get_controlnet_images(controlnet_config: ControlNetReq, height: int, width: int, resize_mode: str):
+    response_images = []
+    control_images = resize_images(controlnet_config.control_images, height, width, resize_mode)
+    for controlnet, image in zip(controlnet_config.controlnets, control_images):
+        if controlnet == "canny":
+            processor = Processor('canny')
+        elif controlnet == "depth":
+            processor = Processor('depth_midas')
+        elif controlnet == "pose":
+            processor = Processor('openpose_full')
+        else:
+            raise ValueError(f"Invalid Controlnet: {controlnet}")
+    
+        response_images.append(processor(image, to_pil=True))
+    
+    return response_images
+
+
+def cleanup(pipeline, loras = None):
+    if loras:
+        pipeline.unload_lora_weights()
+    gc.collect()
+    torch.cuda.empty_cache()

modules/helpers/flux_helpers.py

@@ -0,0 +1,185 @@
+import random
+
+import gradio as gr
+import torch
+from diffusers import (
+    AutoPipelineForText2Image,
+    AutoPipelineForImage2Image,
+    AutoPipelineForInpainting,
+    DiffusionPipeline,
+    AutoencoderKL,
+    FluxControlNetModel,
+    FluxMultiControlNetModel,
+)
+from huggingface_hub import hf_hub_download
+from diffusers.schedulers import *
+from huggingface_hub import hf_hub_download
+from sd_embed.embedding_funcs import get_weighted_text_embeddings_flux1
+
+from .common_helpers import *
+
+
+def load_sd():
+    # device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    # Models
+    models = [
+        {
+            "repo_id": "black-forest-labs/FLUX.1-dev",
+            "loader": "flux",
+            "compute_type": torch.bfloat16,
+        }
+    ]
+
+    for model in models:
+        try:
+            model["pipeline"] = AutoPipelineForText2Image.from_pretrained(
+                model['repo_id'],
+                vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16).to(device),
+                torch_dtype = model['compute_type'],
+                safety_checker = None,
+                variant = "fp16"
+            ).to(device)
+        except:
+            model["pipeline"] = AutoPipelineForText2Image.from_pretrained(
+                model['repo_id'],
+                vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=torch.bfloat16).to(device),
+                torch_dtype = model['compute_type'],
+                safety_checker = None
+            ).to(device)
+        
+        model["pipeline"].enable_model_cpu_offload()
+
+
+    # VAE n Refiner
+    flux_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=torch.bfloat16).to(device)
+    sdxl_vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16).to(device)
+    refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", vae=sdxl_vae, torch_dtype=torch.float16, use_safetensors=True, variant="fp16").to(device)
+    refiner.enable_model_cpu_offload()
+
+
+    # ControlNet
+    controlnet = FluxMultiControlNetModel([FluxControlNetModel.from_pretrained(
+        "Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro",
+        torch_dtype=torch.bfloat16
+    ).to(device)])
+    
+    return device, models, flux_vae, sdxl_vae, refiner, controlnet
+
+
+device, models, flux_vae, sdxl_vae, refiner, controlnet = load_sd()
+
+
+def get_control_mode(controlnet_config: ControlNetReq):
+    control_mode = []
+    layers = ["canny", "tile", "depth", "blur", "pose", "gray", "low_quality"]
+    
+    for c in controlnet_config.controlnets:
+        if c in layers:
+            control_mode.append(layers.index(c))
+    
+    return control_mode
+
+
+def get_pipe(request: BaseReq | BaseImg2ImgReq | BaseInpaintReq):
+    for m in models:
+        if m['repo_id'] == request.model:
+            pipe_args = {
+                "pipeline": m['pipeline'],
+            }
+            
+            
+            # Set ControlNet config
+            if request.controlnet_config:
+                pipe_args["control_mode"] = get_control_mode(request.controlnet_config)
+                pipe_args["controlnet"] = [controlnet]
+            
+            
+            # Choose Pipeline Mode
+            if isinstance(request, BaseReq):
+                pipe_args['pipeline'] = AutoPipelineForText2Image.from_pipe(**pipe_args)
+            elif isinstance(request, BaseImg2ImgReq):
+                pipe_args['pipeline'] = AutoPipelineForImage2Image.from_pipe(**pipe_args)
+            elif isinstance(request, BaseInpaintReq):
+                pipe_args['pipeline'] = AutoPipelineForInpainting.from_pipe(**pipe_args)
+            
+            
+            # Enable or Disable Refiner
+            if request.vae:
+                pipe_args["pipeline"].vae = flux_vae
+            elif not request.vae:
+                pipe_args["pipeline"].vae = None
+            
+            
+            # Set Scheduler
+            pipe_args["pipeline"].scheduler = FlowMatchEulerDiscreteScheduler.from_config(pipe_args["pipeline"].scheduler.config)
+            
+            
+            # Set Loras
+            if request.loras:
+                for i, lora in enumerate(request.loras):
+                    pipe_args["pipeline"].load_lora_weights(request.lora['repo_id'], adapter_name=f"lora_{i}")
+                adapter_names = [f"lora_{i}" for i in range(len(request.loras))]
+                adapter_weights = [lora['weight'] for lora in request.loras]
+                
+                if request.fast_generation:
+                    hyper_lora = hf_hub_download("ByteDance/Hyper-SD", "Hyper-FLUX.1-dev-8steps-lora.safetensors")
+                    hyper_weight = 0.125
+                    pipe_args["pipeline"].load_lora_weights(hyper_lora, adapter_name="hyper_lora")
+                    adapter_names.append("hyper_lora")
+                    adapter_weights.append(hyper_weight)
+                
+                pipe_args["pipeline"].set_adapters(adapter_names, adapter_weights)
+            
+            return pipe_args
+
+
+def get_prompt_attention(pipeline, prompt):
+    return get_weighted_text_embeddings_flux1(pipeline, prompt)
+
+
+# Gen Function
+def gen_img(request: BaseReq | BaseImg2ImgReq | BaseInpaintReq):
+    pipe_args = get_pipe(request)
+    pipeline = pipe_args["pipeline"]
+    try:
+        positive_prompt_embeds, positive_prompt_pooled = get_prompt_attention(pipeline, request.prompt)
+        
+        # Common Args
+        args = {
+            'prompt_embeds': positive_prompt_embeds,
+            'pooled_prompt_embeds': positive_prompt_pooled,
+            'height': request.height,
+            'width': request.width,
+            'num_images_per_prompt': request.num_images_per_prompt,
+            'num_inference_steps': request.num_inference_steps,
+            'guidance_scale': request.guidance_scale,
+            'generator': [torch.Generator(device=device).manual_seed(request.seed + i) if not request.seed is any([None, 0, -1]) else torch.Generator(device=device).manual_seed(random.randint(0, 2**32 - 1)) for i in range(request.num_images_per_prompt)],
+        }
+        
+        if request.controlnet_config:
+            args['control_mode'] = get_control_mode(request.controlnet_config)
+            args['control_images'] = get_controlnet_images(request.controlnet_config, request.height, request.width, request.resize_mode)
+            args['controlnet_conditioning_scale'] = request.controlnet_config.controlnet_conditioning_scale
+        
+        if isinstance(request, (BaseImg2ImgReq, BaseInpaintReq)):
+            args['image'] = resize_images([request.image], request.height, request.width, request.resize_mode)[0]
+            args['strength'] = request.strength
+        
+        if isinstance(request, BaseInpaintReq):
+            args['mask_image'] = resize_images([request.mask_image], request.height, request.width, request.resize_mode)[0]
+        
+        # Generate
+        images = pipeline(**args).images
+        
+        # Refiner
+        if request.refiner:
+            images = refiner(image=images, prompt=request.prompt, num_inference_steps=40, denoising_start=0.7).images
+        
+        cleanup(pipeline, request.loras)
+        
+        return images
+    except Exception as e:
+        cleanup(pipeline, request.loras)
+        raise gr.Error(f"Error: {e}")

modules/pipelines/flux_pipelines.py

@@ -0,0 +1,19 @@
+# modules/pipelines/flux_pipelines.py
+
+import torch
+from diffusers import AutoPipelineForText2Image, AutoencoderKL
+
+def load_flux():
+    # Load FLUX models and pipelines
+    # ...
+    return device, models, flux_vae, controlnet
+
+# modules/pipelines/sdxl_pipelines.py
+
+import torch
+from diffusers import AutoPipelineForText2Image, AutoencoderKL
+
+def load_sdxl():
+    # Load SDXL models and pipelines
+    # ...
+    return device, models, sdxl_vae, controlnet

old/app.py

@@ -0,0 +1,816 @@
+# Testing one file gradio app for zero gpu spaces not working as expected.
+# Check here for the issue: https://huggingface.co/spaces/zero-gpu-explorers/README/discussions/106#66e278a396acd45223e0d00b
+
+import os
+import gc
+import json
+import random
+from typing import List, Optional
+
+import spaces
+import gradio as gr
+from huggingface_hub import ModelCard
+import torch
+from pydantic import BaseModel
+from PIL import Image
+from diffusers import (
+    AutoPipelineForText2Image,
+    AutoPipelineForImage2Image,
+    AutoPipelineForInpainting,
+    DiffusionPipeline,
+    AutoencoderKL,
+    FluxControlNetModel,
+    FluxMultiControlNetModel,
+)
+from huggingface_hub import hf_hub_download
+from diffusers.schedulers import *
+from huggingface_hub import hf_hub_download
+from controlnet_aux.processor import Processor
+from sd_embed.embedding_funcs import get_weighted_text_embeddings_flux1
+
+
+# Initialize System
+os.system("pip install --upgrade pip")
+
+
+def load_sd():
+    # device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    # Models
+    models = [
+        {
+            "repo_id": "black-forest-labs/FLUX.1-dev",
+            "loader": "flux",
+            "compute_type": torch.bfloat16,
+        }
+    ]
+
+    for model in models:
+        try:
+            model["pipeline"] = AutoPipelineForText2Image.from_pretrained(
+                model['repo_id'],
+                vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16).to(device),
+                torch_dtype = model['compute_type'],
+                safety_checker = None,
+                variant = "fp16"
+            ).to(device)
+        except:
+            model["pipeline"] = AutoPipelineForText2Image.from_pretrained(
+                model['repo_id'],
+                vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=torch.bfloat16).to(device),
+                torch_dtype = model['compute_type'],
+                safety_checker = None
+            ).to(device)
+        
+        model["pipeline"].enable_model_cpu_offload()
+
+
+    # VAE n Refiner
+    flux_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=torch.bfloat16).to(device)
+    sdxl_vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16).to(device)
+    refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", vae=sdxl_vae, torch_dtype=torch.float16, use_safetensors=True, variant="fp16").to(device)
+    refiner.enable_model_cpu_offload()
+
+
+    # ControlNet
+    controlnet = FluxMultiControlNetModel([FluxControlNetModel.from_pretrained(
+        "Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro",
+        torch_dtype=torch.bfloat16
+    ).to(device)])
+    
+    return device, models, flux_vae, sdxl_vae, refiner, controlnet
+
+
+device, models, flux_vae, sdxl_vae, refiner, controlnet = load_sd()
+
+
+# Models
+class ControlNetReq(BaseModel):
+    controlnets: List[str] # ["canny", "tile", "depth"]
+    control_images: List[Image.Image]
+    controlnet_conditioning_scale: List[float]
+    
+    class Config:
+        arbitrary_types_allowed=True
+
+
+class FluxReq(BaseModel):
+    model: str = ""
+    prompt: str = ""
+    fast_generation: Optional[bool] = True
+    loras: Optional[list] = []
+    resize_mode: Optional[str] = "resize_and_fill" # resize_only, crop_and_resize, resize_and_fill
+    scheduler: Optional[str] = "euler_fl"
+    height: int = 1024
+    width: int = 1024
+    num_images_per_prompt: int = 1
+    num_inference_steps: int = 8
+    guidance_scale: float = 3.5
+    seed: Optional[int] = 0
+    refiner: bool = False
+    vae: bool = True
+    controlnet_config: Optional[ControlNetReq] = None
+    
+    class Config:
+        arbitrary_types_allowed=True
+
+
+class FluxImg2ImgReq(FluxReq):
+    image: Image.Image
+    strength: float = 1.0
+    
+    class Config:
+        arbitrary_types_allowed=True
+
+
+class FluxInpaintReq(FluxImg2ImgReq):
+    mask_image: Image.Image
+    
+    class Config:
+        arbitrary_types_allowed=True
+
+
+# Helper Functions
+def get_control_mode(controlnet_config: ControlNetReq):
+    control_mode = []
+    layers = ["canny", "tile", "depth", "blur", "pose", "gray", "low_quality"]
+    
+    for c in controlnet_config.controlnets:
+        if c in layers:
+            control_mode.append(layers.index(c))
+    
+    return control_mode
+
+
+def get_pipe(request: FluxReq | FluxImg2ImgReq | FluxInpaintReq):
+    for m in models:
+        if m['repo_id'] == request.model:
+            pipe_args = {
+                "pipeline": m['pipeline'],
+            }
+            
+            
+            # Set ControlNet config
+            if request.controlnet_config:
+                pipe_args["control_mode"] = get_control_mode(request.controlnet_config)
+                pipe_args["controlnet"] = [controlnet]
+            
+            
+            # Choose Pipeline Mode
+            if isinstance(request, FluxReq):
+                pipe_args['pipeline'] = AutoPipelineForText2Image.from_pipe(**pipe_args)
+            elif isinstance(request, FluxImg2ImgReq):
+                pipe_args['pipeline'] = AutoPipelineForImage2Image.from_pipe(**pipe_args)
+            elif isinstance(request, FluxInpaintReq):
+                pipe_args['pipeline'] = AutoPipelineForInpainting.from_pipe(**pipe_args)
+            
+            
+            # Enable or Disable Refiner
+            if request.vae:
+                pipe_args["pipeline"].vae = flux_vae
+            elif not request.vae:
+                pipe_args["pipeline"].vae = None
+            
+            
+            # Set Scheduler
+            pipe_args["pipeline"].scheduler = FlowMatchEulerDiscreteScheduler.from_config(pipe_args["pipeline"].scheduler.config)
+            
+            
+            # Set Loras
+            if request.loras:
+                for i, lora in enumerate(request.loras):
+                    pipe_args["pipeline"].load_lora_weights(request.lora['repo_id'], adapter_name=f"lora_{i}")
+                adapter_names = [f"lora_{i}" for i in range(len(request.loras))]
+                adapter_weights = [lora['weight'] for lora in request.loras]
+                
+                if request.fast_generation:
+                    hyper_lora = hf_hub_download("ByteDance/Hyper-SD", "Hyper-FLUX.1-dev-8steps-lora.safetensors")
+                    hyper_weight = 0.125
+                    pipe_args["pipeline"].load_lora_weights(hyper_lora, adapter_name="hyper_lora")
+                    adapter_names.append("hyper_lora")
+                    adapter_weights.append(hyper_weight)
+                
+                pipe_args["pipeline"].set_adapters(adapter_names, adapter_weights)
+            
+            return pipe_args
+
+
+def resize_images(images: List[Image.Image], height: int, width: int, resize_mode: str):
+    for image in images:
+        if resize_mode == "resize_only":
+            image = image.resize((width, height))
+        elif resize_mode == "crop_and_resize":
+            image = image.crop((0, 0, width, height))
+        elif resize_mode == "resize_and_fill":
+            image = image.resize((width, height), Image.Resampling.LANCZOS)
+
+    return images
+
+
+def get_controlnet_images(controlnet_config: ControlNetReq, height: int, width: int, resize_mode: str):
+    response_images = []
+    control_images = resize_images(controlnet_config.control_images, height, width, resize_mode)
+    for controlnet, image in zip(controlnet_config.controlnets, control_images):
+        if controlnet == "canny":
+            processor = Processor('canny')
+        elif controlnet == "depth":
+            processor = Processor('depth_midas')
+        elif controlnet == "pose":
+            processor = Processor('openpose_full')
+        else:
+            raise ValueError(f"Invalid Controlnet: {controlnet}")
+    
+        response_images.append(processor(image, to_pil=True))
+    
+    return response_images
+
+
+def get_prompt_attention(pipeline, prompt):
+    return get_weighted_text_embeddings_flux1(pipeline, prompt)
+
+
+def cleanup(pipeline, loras = None):
+    if loras:
+        pipeline.unload_lora_weights()
+    gc.collect()
+    torch.cuda.empty_cache()
+
+
+# Gen Function
+def gen_img(request: FluxReq | FluxImg2ImgReq | FluxInpaintReq):
+    pipe_args = get_pipe(request)
+    pipeline = pipe_args["pipeline"]
+    try:
+        positive_prompt_embeds, positive_prompt_pooled = get_prompt_attention(pipeline, request.prompt)
+        
+        # Common Args
+        args = {
+            'prompt_embeds': positive_prompt_embeds,
+            'pooled_prompt_embeds': positive_prompt_pooled,
+            'height': request.height,
+            'width': request.width,
+            'num_images_per_prompt': request.num_images_per_prompt,
+            'num_inference_steps': request.num_inference_steps,
+            'guidance_scale': request.guidance_scale,
+            'generator': [torch.Generator(device=device).manual_seed(request.seed + i) if not request.seed is any([None, 0, -1]) else torch.Generator(device=device).manual_seed(random.randint(0, 2**32 - 1)) for i in range(request.num_images_per_prompt)],
+        }
+        
+        if request.controlnet_config:
+            args['control_mode'] = get_control_mode(request.controlnet_config)
+            args['control_images'] = get_controlnet_images(request.controlnet_config, request.height, request.width, request.resize_mode)
+            args['controlnet_conditioning_scale'] = request.controlnet_config.controlnet_conditioning_scale
+        
+        if isinstance(request, (FluxImg2ImgReq, FluxInpaintReq)):
+            args['image'] = resize_images([request.image], request.height, request.width, request.resize_mode)[0]
+            args['strength'] = request.strength
+        
+        if isinstance(request, FluxInpaintReq):
+            args['mask_image'] = resize_images([request.mask_image], request.height, request.width, request.resize_mode)[0]
+        
+        # Generate
+        images = pipeline(**args).images
+        
+        # Refiner
+        if request.refiner:
+            images = refiner(image=images, prompt=request.prompt, num_inference_steps=40, denoising_start=0.7).images
+        
+        cleanup(pipeline, request.loras)
+        
+        return images
+    except Exception as e:
+        cleanup(pipeline, request.loras)
+        raise gr.Error(f"Error: {e}")
+
+
+
+# CSS
+css = """
+@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;600&display=swap');
+body {
+    font-family: 'Poppins', sans-serif !important;
+}
+.center-content {
+    text-align: center;
+    max-width: 600px;
+    margin: 0 auto;
+    padding: 20px;
+}
+.center-content h1 {
+    font-weight: 600;
+    margin-bottom: 1rem;
+}
+.center-content p {
+    margin-bottom: 1.5rem;
+}
+"""
+
+
+flux_models = ["black-forest-labs/FLUX.1-dev"]
+with open("data/images/loras/flux.json", "r") as f:
+    loras = json.load(f)
+
+
+# Event functions
+def update_fast_generation(model, fast_generation):
+    if fast_generation:
+        return (
+            gr.update(
+                value=3.5
+            ),
+            gr.update(
+                value=8
+            )
+        )
+
+
+def selected_lora_from_gallery(evt: gr.SelectData):
+    return (
+        gr.update(
+            value=evt.index
+        )
+    )
+
+
+def update_selected_lora(custom_lora):
+    link = custom_lora.split("/")
+    
+    if len(link) == 2:
+        model_card = ModelCard.load(custom_lora)
+        trigger_word = model_card.data.get("instance_prompt", "")
+        image_url = f"""https://huggingface.co/{custom_lora}/resolve/main/{model_card.data.get("widget", [{}])[0].get("output", {}).get("url", None)}"""
+        
+        custom_lora_info_css = """
+        <style>
+            .custom-lora-info {
+                font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Oxygen', 'Ubuntu', 'Cantarell', 'Fira Sans', 'Droid Sans', 'Helvetica Neue', sans-serif;
+                background: linear-gradient(135deg, #4a90e2, #7b61ff);
+                color: white;
+                padding: 16px;
+                border-radius: 8px;
+                box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+                margin: 16px 0;
+            }
+            .custom-lora-header {
+                font-size: 18px;
+                font-weight: 600;
+                margin-bottom: 12px;
+            }
+            .custom-lora-content {
+                display: flex;
+                align-items: center;
+                background-color: rgba(255, 255, 255, 0.1);
+                border-radius: 6px;
+                padding: 12px;
+            }
+            .custom-lora-image {
+                width: 80px;
+                height: 80px;
+                object-fit: cover;
+                border-radius: 6px;
+                margin-right: 16px;
+            }
+            .custom-lora-text h3 {
+                margin: 0 0 8px 0;
+                font-size: 16px;
+                font-weight: 600;
+            }
+            .custom-lora-text small {
+                font-size: 14px;
+                opacity: 0.9;
+            }
+            .custom-trigger-word {
+                background-color: rgba(255, 255, 255, 0.2);
+                padding: 2px 6px;
+                border-radius: 4px;
+                font-weight: 600;
+            }
+        </style>
+        """
+
+        custom_lora_info_html = f"""
+        <div class="custom-lora-info">
+            <div class="custom-lora-header">Custom LoRA: {custom_lora}</div>
+            <div class="custom-lora-content">
+                <img class="custom-lora-image" src="{image_url}" alt="LoRA preview">
+                <div class="custom-lora-text">
+                    <h3>{link[1].replace("-", " ").replace("_", " ")}</h3>
+                    <small>{"Using: <span class='custom-trigger-word'>"+trigger_word+"</span> as the trigger word" if trigger_word else "No trigger word found. If there's a trigger word, include it in your prompt"}</small>
+                </div>
+            </div>
+        </div>
+        """
+
+        custom_lora_info_html = f"{custom_lora_info_css}{custom_lora_info_html}"
+
+        return (
+            gr.update( # selected_lora
+                value=custom_lora,
+            ),
+            gr.update( # custom_lora_info
+                value=custom_lora_info_html,
+                visible=True
+            )
+        )
+
+    else:
+        return (
+            gr.update( # selected_lora
+                value=custom_lora,
+            ),
+            gr.update( # custom_lora_info
+                value=custom_lora_info_html if len(link) == 0 else "",
+                visible=False
+            )
+        )
+
+
+def add_to_enabled_loras(model, selected_lora, enabled_loras):
+    lora_data = loras
+    try:
+        selected_lora = int(selected_lora)
+        
+        if 0 <= selected_lora: # is the index of the lora in the gallery
+            lora_info = lora_data[selected_lora]
+            enabled_loras.append({
+                "repo_id": lora_info["repo"],
+                "trigger_word": lora_info["trigger_word"]
+            })
+    except ValueError:
+        link = selected_lora.split("/")
+        if len(link) == 2:
+            model_card = ModelCard.load(selected_lora)
+            trigger_word = model_card.data.get("instance_prompt", "")
+            enabled_loras.append({
+                "repo_id": selected_lora,
+                "trigger_word": trigger_word
+            })
+    
+    return (
+        gr.update( # selected_lora
+            value=""
+        ),
+        gr.update( # custom_lora_info
+            value="",
+            visible=False
+        ),
+        gr.update( # enabled_loras
+            value=enabled_loras
+        )
+    )
+
+
+def update_lora_sliders(enabled_loras):
+    sliders = []
+    remove_buttons = []
+    
+    for lora in enabled_loras:
+        sliders.append(
+            gr.update(
+                label=lora.get("repo_id", ""),
+                info=f"Trigger Word: {lora.get('trigger_word', '')}",
+                visible=True,
+                interactive=True
+            )
+        )
+        remove_buttons.append(
+            gr.update(
+                visible=True,
+                interactive=True
+            )
+        )
+    
+    if len(sliders) < 6:
+        for i in range(len(sliders), 6):
+            sliders.append(
+                gr.update(
+                    visible=False
+                )
+            )
+            remove_buttons.append(
+                gr.update(
+                    visible=False
+                )
+            )
+    
+    return *sliders, *remove_buttons
+
+
+def remove_from_enabled_loras(enabled_loras, index):
+    enabled_loras.pop(index)
+    return (
+        gr.update(
+            value=enabled_loras
+        )
+    )
+
+
+@spaces.GPU
+def generate_image(
+        model, prompt, fast_generation, enabled_loras,
+        lora_slider_0, lora_slider_1, lora_slider_2, lora_slider_3, lora_slider_4, lora_slider_5,
+        img2img_image, inpaint_image, canny_image, pose_image, depth_image,
+        img2img_strength, inpaint_strength, canny_strength, pose_strength, depth_strength,
+        resize_mode,
+        scheduler, image_height, image_width, image_num_images_per_prompt,
+        image_num_inference_steps, image_guidance_scale, image_seed,
+        refiner, vae
+    ):
+        base_args = {
+            "model": model,
+            "prompt": prompt,
+            "fast_generation": fast_generation,
+            "loras": None,
+            "resize_mode": resize_mode,
+            "scheduler": scheduler,
+            "height": int(image_height),
+            "width": int(image_width),
+            "num_images_per_prompt": float(image_num_images_per_prompt),
+            "num_inference_steps": float(image_num_inference_steps),
+            "guidance_scale": float(image_guidance_scale),
+            "seed": int(image_seed),
+            "refiner": refiner,
+            "vae": vae,
+            "controlnet_config": None,
+        }
+        base_args = FluxReq(**base_args)
+        
+        if len(enabled_loras) > 0:
+            base_args.loras = []
+            for enabled_lora, slider in zip(enabled_loras, [lora_slider_0, lora_slider_1, lora_slider_2, lora_slider_3, lora_slider_4, lora_slider_5]):
+                if enabled_lora['repo_id']:
+                    base_args.loras.append({
+                        "repo_id": enabled_lora['repo_id'],
+                        "weight": slider
+                    })
+        
+        image = None
+        mask_image = None
+        strength = None
+        
+        if img2img_image:
+            image = img2img_image
+            strength = float(img2img_strength)
+            
+            base_args = FluxImg2ImgReq(
+                **base_args.__dict__,
+                image=image,
+                strength=strength
+            )
+        elif inpaint_image:
+            image = inpaint_image['background'] if not all(pixel == (0, 0, 0) for pixel in list(inpaint_image['background'].getdata())) else None
+            mask_image = inpaint_image['layers'][0] if image else None
+            strength = float(inpaint_strength)
+            
+            if image and mask_image:
+                base_args = FluxInpaintReq(
+                    **base_args.__dict__,
+                    image=image,
+                    mask_image=mask_image,
+                    strength=strength
+                )
+        elif any([canny_image, pose_image, depth_image]):
+            base_args.controlnet_config = ControlNetReq(
+                controlnets=[],
+                control_images=[],
+                controlnet_conditioning_scale=[]
+            )
+            
+            if canny_image:
+                base_args.controlnet_config.controlnets.append("canny")
+                base_args.controlnet_config.control_images.append(canny_image)
+                base_args.controlnet_config.controlnet_conditioning_scale.append(float(canny_strength))
+            if pose_image:
+                base_args.controlnet_config.controlnets.append("pose")
+                base_args.controlnet_config.control_images.append(pose_image)
+                base_args.controlnet_config.controlnet_conditioning_scale.append(float(pose_strength))
+            if depth_image:
+                base_args.controlnet_config.controlnets.append("depth")
+                base_args.controlnet_config.control_images.append(depth_image)
+                base_args.controlnet_config.controlnet_conditioning_scale.append(float(depth_strength))
+        else:
+            base_args = FluxReq(**base_args.__dict__)
+        
+        return gr.update(
+            value=gen_img(base_args),
+            interactive=True
+        )
+
+
+# Main Gradio app
+with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
+    # Header
+    with gr.Column(elem_classes="center-content"):
+        gr.Markdown("""
+        # 🚀 AAI: All AI
+        Unleash your creativity with our multi-modal AI platform.
+        [![Sync code to HF Space](https://github.com/mantrakp04/aai/actions/workflows/hf-space.yml/badge.svg)](https://github.com/mantrakp04/aai/actions/workflows/hf-space.yml)
+        """)
+
+    # Tabs
+    with gr.Tabs():
+        with gr.Tab(label="🖼️ Image"):
+            with gr.Tabs():
+                with gr.Tab("Flux"):
+                    """
+                    Create the image tab for Generative Image Generation Models
+                    
+                    Args:
+                    models: list
+                        A list containing the models repository paths
+                    gap_iol, gap_la, gap_le, gap_eio, gap_io: Optional[List[dict]]
+                        A list of dictionaries containing the title and component for the custom gradio component
+                        Example:
+                        def gr_comp():
+                            gr.Label("Hello World")
+                        
+                        [
+                            {
+                                'title': "Title",
+                                'component': gr_comp()
+                            }
+                        ]
+                    loras: list
+                        A list of dictionaries containing the image and title for the Loras Gallery
+                        Generally a loaded json file from the data folder
+                    
+                    """
+                    def process_gaps(gaps: List[dict]):
+                        for gap in gaps:
+                            with gr.Accordion(gap['title']):
+                                gap['component']
+                    
+                    
+                    with gr.Row():
+                        with gr.Column():
+                            with gr.Group() as image_options:
+                                model = gr.Dropdown(label="Models", choices=flux_models, value=flux_models[0], interactive=True)
+                                prompt = gr.Textbox(lines=5, label="Prompt")
+                                fast_generation = gr.Checkbox(label="Fast Generation (Hyper-SD) 🧪")
+                            
+                            
+                            with gr.Accordion("Loras", open=True): # Lora Gallery
+                                lora_gallery = gr.Gallery(
+                                    label="Gallery",
+                                    value=[(lora['image'], lora['title']) for lora in loras],
+                                    allow_preview=False,
+                                    columns=3,
+                                    rows=3,
+                                    type="pil"
+                                )
+                                
+                                with gr.Group():
+                                    with gr.Column():
+                                        with gr.Row():
+                                            custom_lora = gr.Textbox(label="Custom Lora", info="Enter a Huggingface repo path")
+                                            selected_lora = gr.Textbox(label="Selected Lora", info="Choose from the gallery or enter a custom LoRA")
+                                        
+                                        custom_lora_info = gr.HTML(visible=False)
+                                        add_lora = gr.Button(value="Add LoRA")
+                                        
+                                        enabled_loras = gr.State(value=[])
+                                        with gr.Group():
+                                            with gr.Row():
+                                                for i in range(6): # only support max 6 loras due to inference time
+                                                    with gr.Column():
+                                                        with gr.Column(scale=2):
+                                                            globals()[f"lora_slider_{i}"] = gr.Slider(label=f"LoRA {i+1}", minimum=0, maximum=1, step=0.01, value=0.8, visible=False, interactive=True)
+                                                        with gr.Column():
+                                                            globals()[f"lora_remove_{i}"] = gr.Button(value="Remove LoRA", visible=False)
+
+                            
+                            with gr.Accordion("Embeddings", open=False): # Embeddings
+                                gr.Label("To be implemented")
+                            
+                            
+                            with gr.Accordion("Image Options"): # Image Options
+                                with gr.Tabs():
+                                    image_options = {
+                                        "img2img": "Upload Image",
+                                        "inpaint": "Upload Image",
+                                        "canny": "Upload Image",
+                                        "pose": "Upload Image",
+                                        "depth": "Upload Image",
+                                    }
+                                    
+                                    for image_option, label in image_options.items():
+                                        with gr.Tab(image_option):
+                                            if not image_option in ['inpaint', 'scribble']:
+                                                globals()[f"{image_option}_image"] = gr.Image(label=label, type="pil")
+                                            elif image_option in ['inpaint', 'scribble']:
+                                                globals()[f"{image_option}_image"] = gr.ImageEditor(
+                                                    label=label,
+                                                    image_mode='RGB',
+                                                    layers=False,
+                                                    brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed") if image_option == 'inpaint' else gr.Brush(),
+                                                    interactive=True,
+                                                    type="pil",
+                                                )
+                                            
+                                            # Image Strength (Co-relates to controlnet strength, strength for img2img n inpaint)
+                                            globals()[f"{image_option}_strength"] = gr.Slider(label="Strength", minimum=0, maximum=1, step=0.01, value=1.0, interactive=True)
+                                    
+                                    resize_mode = gr.Radio(
+                                        label="Resize Mode",
+                                        choices=["crop and resize", "resize only", "resize and fill"],
+                                        value="resize and fill",
+                                        interactive=True
+                                    )
+                        
+                        
+                        with gr.Column():
+                            with gr.Group():
+                                output_images = gr.Gallery(
+                                        label="Output Images",
+                                        value=[],
+                                        allow_preview=True,
+                                        type="pil",
+                                        interactive=False,
+                                    )
+                                generate_images = gr.Button(value="Generate Images", variant="primary")            
+                            
+                            with gr.Accordion("Advance Settings", open=True):
+                                with gr.Row():
+                                    scheduler = gr.Dropdown(
+                                        label="Scheduler",
+                                        choices = [
+                                            "fm_euler"
+                                        ],
+                                        value="fm_euler",
+                                        interactive=True
+                                    )
+
+                                with gr.Row():
+                                    for column in range(2):
+                                        with gr.Column():
+                                            options = [
+                                                ("Height", "image_height", 64, 1024, 64, 1024, True),
+                                                ("Width", "image_width", 64, 1024, 64, 1024, True),
+                                                ("Num Images Per Prompt", "image_num_images_per_prompt", 1, 4, 1, 1, True),
+                                                ("Num Inference Steps", "image_num_inference_steps", 1, 100, 1, 20, True),
+                                                ("Clip Skip", "image_clip_skip", 0, 2, 1, 2, False),
+                                                ("Guidance Scale", "image_guidance_scale", 0, 20, 0.5, 3.5, True),
+                                                ("Seed", "image_seed", 0, 100000, 1, random.randint(0, 100000), True),
+                                            ]
+                                            for label, var_name, min_val, max_val, step, value, visible in options[column::2]:
+                                                globals()[var_name] = gr.Slider(label=label, minimum=min_val, maximum=max_val, step=step, value=value, visible=visible, interactive=True)
+                                
+                                with gr.Row():
+                                    refiner = gr.Checkbox(
+                                        label="Refiner 🧪",
+                                        value=False,
+                                    )
+                                    vae = gr.Checkbox(
+                                        label="VAE",
+                                        value=True,
+                                    )
+
+
+                    # Events
+                    # Base Options
+                    fast_generation.change(update_fast_generation, [model, fast_generation], [image_guidance_scale, image_num_inference_steps]) # Fast Generation # type: ignore
+                    
+
+                    # Lora Gallery
+                    lora_gallery.select(selected_lora_from_gallery, None, selected_lora)
+                    custom_lora.change(update_selected_lora, custom_lora, [custom_lora, selected_lora])
+                    add_lora.click(add_to_enabled_loras, [model, selected_lora, enabled_loras], [selected_lora, custom_lora_info, enabled_loras])
+                    enabled_loras.change(update_lora_sliders, enabled_loras, [lora_slider_0, lora_slider_1, lora_slider_2, lora_slider_3, lora_slider_4, lora_slider_5, lora_remove_0, lora_remove_1, lora_remove_2, lora_remove_3, lora_remove_4, lora_remove_5]) # type: ignore
+
+                    for i in range(6):
+                        globals()[f"lora_remove_{i}"].click(
+                            lambda enabled_loras, index=i: remove_from_enabled_loras(enabled_loras, index),
+                            [enabled_loras],
+                            [enabled_loras]
+                        )
+                    
+
+                    # Generate Image
+                    generate_images.click(
+                        generate_image, # type: ignore
+                        [
+                            model, prompt, fast_generation, enabled_loras,
+                            lora_slider_0, lora_slider_1, lora_slider_2, lora_slider_3, lora_slider_4, lora_slider_5, # type: ignore
+                            img2img_image, inpaint_image, canny_image, pose_image, depth_image, # type: ignore
+                            img2img_strength, inpaint_strength, canny_strength, pose_strength, depth_strength, # type: ignore
+                            resize_mode,
+                            scheduler, image_height, image_width, image_num_images_per_prompt, # type: ignore
+                            image_num_inference_steps, image_guidance_scale, image_seed, # type: ignore
+                            refiner, vae
+                        ],
+                        [output_images]
+                    )
+                with gr.Tab("SDXL"):
+                    gr.Label("To be implemented")
+        with gr.Tab(label="🎵 Audio"):
+            gr.Label("Coming soon!")
+        with gr.Tab(label="🎬 Video"):
+            gr.Label("Coming soon!")
+        with gr.Tab(label="📄 Text"):
+            gr.Label("Coming soon!")
+
+
+demo.launch(
+    share=False,
+    debug=True,
+)

tabs/audio_tab.py

@@ -0,0 +1,5 @@
+import gradio as gr
+
+
+def audio_tab():
+    gr.Label("Coming soon...")

tabs/image_tab.py

@@ -0,0 +1,183 @@
+# tabs/image_tab.py
+
+import gradio as gr
+from modules.events.flux_events import *
+from modules.events.sdxl_events import *
+from modules.helpers.common_helpers import *
+from modules.helpers.flux_helpers import *
+from modules.helpers.sdxl_helpers import *
+from config import flux_models, sdxl_models, loras
+
+
+def image_tab():
+    with gr.Tab(label="🖼️ Image"):
+        with gr.Tabs():
+            flux_tab()
+            sdxl_tab()
+
+
+def flux_tab():
+    with gr.Tab("Flux"):
+        with gr.Row():
+            with gr.Column():
+                with gr.Group() as image_options:
+                    model = gr.Dropdown(label="Models", choices=flux_models, value=flux_models[0], interactive=True)
+                    prompt = gr.Textbox(lines=5, label="Prompt")
+                    fast_generation = gr.Checkbox(label="Fast Generation (Hyper-SD) 🧪")
+                
+                
+                with gr.Accordion("Loras", open=True): # Lora Gallery
+                    lora_gallery = gr.Gallery(
+                        label="Gallery",
+                        value=[(lora['image'], lora['title']) for lora in loras],
+                        allow_preview=False,
+                        columns=3,
+                        rows=3,
+                        type="pil"
+                    )
+                    
+                    with gr.Group():
+                        with gr.Column():
+                            with gr.Row():
+                                custom_lora = gr.Textbox(label="Custom Lora", info="Enter a Huggingface repo path")
+                                selected_lora = gr.Textbox(label="Selected Lora", info="Choose from the gallery or enter a custom LoRA")
+                            
+                            custom_lora_info = gr.HTML(visible=False)
+                            add_lora = gr.Button(value="Add LoRA")
+                            
+                            enabled_loras = gr.State(value=[])
+                            with gr.Group():
+                                with gr.Row():
+                                    for i in range(6): # only support max 6 loras due to inference time
+                                        with gr.Column():
+                                            with gr.Column(scale=2):
+                                                globals()[f"lora_slider_{i}"] = gr.Slider(label=f"LoRA {i+1}", minimum=0, maximum=1, step=0.01, value=0.8, visible=False, interactive=True)
+                                            with gr.Column():
+                                                globals()[f"lora_remove_{i}"] = gr.Button(value="Remove LoRA", visible=False)
+
+                
+                with gr.Accordion("Embeddings", open=False): # Embeddings
+                    gr.Label("To be implemented")
+                
+                
+                with gr.Accordion("Image Options"): # Image Options
+                    with gr.Tabs():
+                        image_options = {
+                            "img2img": "Upload Image",
+                            "inpaint": "Upload Image",
+                            "canny": "Upload Image",
+                            "pose": "Upload Image",
+                            "depth": "Upload Image",
+                        }
+                        
+                        for image_option, label in image_options.items():
+                            with gr.Tab(image_option):
+                                if not image_option in ['inpaint', 'scribble']:
+                                    globals()[f"{image_option}_image"] = gr.Image(label=label, type="pil")
+                                elif image_option in ['inpaint', 'scribble']:
+                                    globals()[f"{image_option}_image"] = gr.ImageEditor(
+                                        label=label,
+                                        image_mode='RGB',
+                                        layers=False,
+                                        brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed") if image_option == 'inpaint' else gr.Brush(),
+                                        interactive=True,
+                                        type="pil",
+                                    )
+                                
+                                # Image Strength (Co-relates to controlnet strength, strength for img2img n inpaint)
+                                globals()[f"{image_option}_strength"] = gr.Slider(label="Strength", minimum=0, maximum=1, step=0.01, value=1.0, interactive=True)
+                        
+                        resize_mode = gr.Radio(
+                            label="Resize Mode",
+                            choices=["crop and resize", "resize only", "resize and fill"],
+                            value="resize and fill",
+                            interactive=True
+                        )
+            
+            
+            with gr.Column():
+                with gr.Group():
+                    output_images = gr.Gallery(
+                            label="Output Images",
+                            value=[],
+                            allow_preview=True,
+                            type="pil",
+                            interactive=False,
+                        )
+                    generate_images = gr.Button(value="Generate Images", variant="primary")            
+                
+                with gr.Accordion("Advance Settings", open=True):
+                    with gr.Row():
+                        scheduler = gr.Dropdown(
+                            label="Scheduler",
+                            choices = [
+                                "fm_euler"
+                            ],
+                            value="fm_euler",
+                            interactive=True
+                        )
+
+                    with gr.Row():
+                        for column in range(2):
+                            with gr.Column():
+                                options = [
+                                    ("Height", "image_height", 64, 1024, 64, 1024, True),
+                                    ("Width", "image_width", 64, 1024, 64, 1024, True),
+                                    ("Num Images Per Prompt", "image_num_images_per_prompt", 1, 4, 1, 1, True),
+                                    ("Num Inference Steps", "image_num_inference_steps", 1, 100, 1, 20, True),
+                                    ("Clip Skip", "image_clip_skip", 0, 2, 1, 2, False),
+                                    ("Guidance Scale", "image_guidance_scale", 0, 20, 0.5, 3.5, True),
+                                    ("Seed", "image_seed", 0, 100000, 1, random.randint(0, 100000), True),
+                                ]
+                                for label, var_name, min_val, max_val, step, value, visible in options[column::2]:
+                                    globals()[var_name] = gr.Slider(label=label, minimum=min_val, maximum=max_val, step=step, value=value, visible=visible, interactive=True)
+                    
+                    with gr.Row():
+                        refiner = gr.Checkbox(
+                            label="Refiner 🧪",
+                            value=False,
+                        )
+                        vae = gr.Checkbox(
+                            label="VAE",
+                            value=True,
+                        )
+    
+    # Events
+    # Base Options
+    fast_generation.change(update_fast_generation, [model, fast_generation], [image_guidance_scale, image_num_inference_steps]) # Fast Generation # type: ignore
+    
+
+    # Lora Gallery
+    lora_gallery.select(selected_lora_from_gallery, None, selected_lora)
+    custom_lora.change(update_selected_lora, custom_lora, [custom_lora, selected_lora])
+    add_lora.click(add_to_enabled_loras, [model, selected_lora, enabled_loras], [selected_lora, custom_lora_info, enabled_loras])
+    enabled_loras.change(update_lora_sliders, enabled_loras, [lora_slider_0, lora_slider_1, lora_slider_2, lora_slider_3, lora_slider_4, lora_slider_5, lora_remove_0, lora_remove_1, lora_remove_2, lora_remove_3, lora_remove_4, lora_remove_5]) # type: ignore
+
+    for i in range(6):
+        globals()[f"lora_remove_{i}"].click(
+            lambda enabled_loras, index=i: remove_from_enabled_loras(enabled_loras, index),
+            [enabled_loras],
+            [enabled_loras]
+        )
+    
+
+    # Generate Image
+    generate_images.click(
+        generate_image, # type: ignore
+        [
+            model, prompt, fast_generation, enabled_loras,
+            lora_slider_0, lora_slider_1, lora_slider_2, lora_slider_3, lora_slider_4, lora_slider_5, # type: ignore
+            img2img_image, inpaint_image, canny_image, pose_image, depth_image, # type: ignore
+            img2img_strength, inpaint_strength, canny_strength, pose_strength, depth_strength, # type: ignore
+            resize_mode,
+            scheduler, image_height, image_width, image_num_images_per_prompt, # type: ignore
+            image_num_inference_steps, image_guidance_scale, image_seed, # type: ignore
+            refiner, vae
+        ],
+        [output_images]
+    )
+
+
+def sdxl_tab():
+    with gr.Tab("SDXL"):
+        gr.Label("To be implemented")

tabs/text_tab.py

@@ -0,0 +1,5 @@
+import gradio as gr
+
+
+def text_tab():
+    gr.Label("Coming soon...")

tabs/video_tab.py

@@ -0,0 +1,5 @@
+import gradio as gr
+
+
+def video_tab():
+    gr.Label("Coming soon...")