app.py

"""
Lyra/Lune Flow-Matching Inference Space
Author: AbstractPhil
License: MIT

SD1.5-based flow matching with geometric crystalline architectures.
"""

import os
import torch
import gradio as gr
import numpy as np
from PIL import Image
from typing import Optional, Dict
import spaces

from diffusers import (
    UNet2DConditionModel,
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    EulerDiscreteScheduler
)
from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer
from huggingface_hub import hf_hub_download

# Import Lyra VAE from geovocab2
try:
    from geovocab2.train.model.vae.vae_lyra import MultiModalVAE, MultiModalVAEConfig
    LYRA_AVAILABLE = True
except ImportError:
    print("⚠️ Lyra VAE not available - install geovocab2")
    LYRA_AVAILABLE = False


# ============================================================================
# MODEL LOADING
# ============================================================================

class FlowMatchingPipeline:
    """Custom pipeline for flow-matching inference."""
    
    def __init__(
        self,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        tokenizer: CLIPTokenizer,
        unet: UNet2DConditionModel,
        scheduler,
        device: str = "cuda",
        t5_encoder: Optional[T5EncoderModel] = None,
        t5_tokenizer: Optional[T5Tokenizer] = None,
        lyra_model: Optional[any] = None
    ):
        self.vae = vae
        self.text_encoder = text_encoder
        self.tokenizer = tokenizer
        self.unet = unet
        self.scheduler = scheduler
        self.device = device
        
        # Lyra-specific components
        self.t5_encoder = t5_encoder
        self.t5_tokenizer = t5_tokenizer
        self.lyra_model = lyra_model
        
        # VAE scaling factor
        self.vae_scale_factor = 0.18215
        
    def encode_prompt(self, prompt: str, negative_prompt: str = ""):
        """Encode text prompts to embeddings."""
        # Positive prompt
        text_inputs = self.tokenizer(
            prompt,
            padding="max_length",
            max_length=self.tokenizer.model_max_length,
            truncation=True,
            return_tensors="pt",
        )
        text_input_ids = text_inputs.input_ids.to(self.device)
        
        with torch.no_grad():
            prompt_embeds = self.text_encoder(text_input_ids)[0]
        
        # Negative prompt
        if negative_prompt:
            uncond_inputs = self.tokenizer(
                negative_prompt,
                padding="max_length",
                max_length=self.tokenizer.model_max_length,
                truncation=True,
                return_tensors="pt",
            )
            uncond_input_ids = uncond_inputs.input_ids.to(self.device)
            
            with torch.no_grad():
                negative_prompt_embeds = self.text_encoder(uncond_input_ids)[0]
        else:
            negative_prompt_embeds = torch.zeros_like(prompt_embeds)
        
        return prompt_embeds, negative_prompt_embeds
    
    def encode_prompt_lyra(self, prompt: str, negative_prompt: str = ""):
        """Encode text prompts using Lyra VAE (CLIP + T5 fusion)."""
        if self.lyra_model is None or self.t5_encoder is None:
            raise ValueError("Lyra VAE components not initialized")
        
        # Get CLIP embeddings
        text_inputs = self.tokenizer(
            prompt,
            padding="max_length",
            max_length=self.tokenizer.model_max_length,
            truncation=True,
            return_tensors="pt",
        )
        text_input_ids = text_inputs.input_ids.to(self.device)
        
        with torch.no_grad():
            clip_embeds = self.text_encoder(text_input_ids)[0]
        
        # Get T5 embeddings
        t5_inputs = self.t5_tokenizer(
            prompt,
            max_length=77,
            padding='max_length',
            truncation=True,
            return_tensors='pt'
        ).to(self.device)
        
        with torch.no_grad():
            t5_embeds = self.t5_encoder(**t5_inputs).last_hidden_state
        
        # Fuse through Lyra VAE
        modality_inputs = {
            'clip': clip_embeds,
            't5': t5_embeds
        }
        
        with torch.no_grad():
            reconstructions, mu, logvar = self.lyra_model(
                modality_inputs, 
                target_modalities=['clip']
            )
            prompt_embeds = reconstructions['clip']
        
        # Process negative prompt
        if negative_prompt:
            uncond_inputs = self.tokenizer(
                negative_prompt,
                padding="max_length",
                max_length=self.tokenizer.model_max_length,
                truncation=True,
                return_tensors="pt",
            )
            uncond_input_ids = uncond_inputs.input_ids.to(self.device)
            
            with torch.no_grad():
                clip_embeds_uncond = self.text_encoder(uncond_input_ids)[0]
            
            t5_inputs_uncond = self.t5_tokenizer(
                negative_prompt,
                max_length=77,
                padding='max_length',
                truncation=True,
                return_tensors='pt'
            ).to(self.device)
            
            with torch.no_grad():
                t5_embeds_uncond = self.t5_encoder(**t5_inputs_uncond).last_hidden_state
            
            modality_inputs_uncond = {
                'clip': clip_embeds_uncond,
                't5': t5_embeds_uncond
            }
            
            with torch.no_grad():
                reconstructions_uncond, _, _ = self.lyra_model(
                    modality_inputs_uncond,
                    target_modalities=['clip']
                )
                negative_prompt_embeds = reconstructions_uncond['clip']
        else:
            negative_prompt_embeds = torch.zeros_like(prompt_embeds)
        
        return prompt_embeds, negative_prompt_embeds
    
    @torch.no_grad()
    def __call__(
        self,
        prompt: str,
        negative_prompt: str = "",
        height: int = 512,
        width: int = 512,
        num_inference_steps: int = 20,
        guidance_scale: float = 7.5,
        shift: float = 2.5,
        use_flow_matching: bool = True,
        prediction_type: str = "epsilon",
        seed: Optional[int] = None,
        use_lyra: bool = False,
        progress_callback=None
    ):
        """Generate image using flow matching or standard diffusion."""
        
        # Set seed
        if seed is not None:
            generator = torch.Generator(device=self.device).manual_seed(seed)
        else:
            generator = None
        
        # Encode prompts - use Lyra if specified
        if use_lyra and self.lyra_model is not None:
            prompt_embeds, negative_prompt_embeds = self.encode_prompt_lyra(
                prompt, negative_prompt
            )
        else:
            prompt_embeds, negative_prompt_embeds = self.encode_prompt(
                prompt, negative_prompt
            )
        
        # Prepare latents
        latent_channels = 4
        latent_height = height // 8
        latent_width = width // 8
        
        latents = torch.randn(
            (1, latent_channels, latent_height, latent_width),
            generator=generator,
            device=self.device,
            dtype=torch.float32
        )
        
        # Set timesteps
        self.scheduler.set_timesteps(num_inference_steps, device=self.device)
        timesteps = self.scheduler.timesteps
        
        # Scale initial latents by scheduler's init_noise_sigma for standard diffusion
        # Flow matching uses unscaled latents and custom ODE integration
        if not use_flow_matching:
            latents = latents * self.scheduler.init_noise_sigma
        
        # Denoising loop
        for i, t in enumerate(timesteps):
            if progress_callback:
                progress_callback(i, num_inference_steps, f"Step {i+1}/{num_inference_steps}")
            
            # Expand latents for classifier-free guidance
            latent_model_input = torch.cat([latents] * 2) if guidance_scale > 1.0 else latents
            
            # For standard diffusion, let scheduler handle scaling
            # For flow matching, apply custom shift-based scaling
            if use_flow_matching and shift > 0:
                # Compute sigma from timestep with shift
                sigma = t.float() / 1000.0
                sigma_shifted = (shift * sigma) / (1 + (shift - 1) * sigma)
                
                # Scale latent input for flow matching
                scaling = torch.sqrt(1 + sigma_shifted ** 2)
                latent_model_input = latent_model_input / scaling
            else:
                # For standard diffusion, scale by scheduler
                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
            
            # Prepare timestep
            timestep = t.expand(latent_model_input.shape[0])
            
            # Predict noise/velocity
            text_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) if guidance_scale > 1.0 else prompt_embeds
            
            noise_pred = self.unet(
                latent_model_input,
                timestep,
                encoder_hidden_states=text_embeds,
                return_dict=False
            )[0]
            
            # Classifier-free guidance
            if guidance_scale > 1.0:
                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
            
            # Flow matching step
            if use_flow_matching:
                # Manual flow matching update
                sigma = t.float() / 1000.0
                sigma_shifted = (shift * sigma) / (1 + (shift - 1) * sigma)
                
                if prediction_type == "v_prediction":
                    # Convert v-prediction to epsilon
                    v_pred = noise_pred
                    alpha_t = torch.sqrt(1 - sigma_shifted ** 2)
                    sigma_t = sigma_shifted
                    noise_pred = alpha_t * v_pred + sigma_t * latents
                
                # Compute next latent
                dt = -1.0 / num_inference_steps
                latents = latents + dt * noise_pred
            else:
                # Standard scheduler step
                latents = self.scheduler.step(
                    noise_pred, t, latents, return_dict=False
                )[0]
        
        # Decode latents with model-specific scaling
        latents = latents / self.vae_scale_factor
        
        # Lune-specific scaling: multiply by 5.52 for Lune's latent space offset
        # This must be applied ONLY for Lune model, not SD1.5 Base
        if hasattr(self, 'is_lune_model') and self.is_lune_model:
            latents = latents * 5.52
        
        with torch.no_grad():
            image = self.vae.decode(latents).sample
        
        # Convert to PIL
        image = (image / 2 + 0.5).clamp(0, 1)
        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
        image = (image * 255).round().astype("uint8")
        image = Image.fromarray(image[0])
        
        return image


def load_lune_checkpoint(repo_id: str, filename: str, device: str = "cuda"):
    """Load Lune checkpoint from .pt file."""
    print(f"📥 Downloading checkpoint: {repo_id}/{filename}")
    
    checkpoint_path = hf_hub_download(
        repo_id=repo_id,
        filename=filename,
        repo_type="model"
    )
    
    print(f"✓ Downloaded to: {checkpoint_path}")
    print(f"📦 Loading checkpoint...")
    
    checkpoint = torch.load(checkpoint_path, map_location="cpu")
    
    # Initialize UNet with SD1.5 config
    print(f"🏗️ Initializing SD1.5 UNet...")
    unet = UNet2DConditionModel.from_pretrained(
        "runwayml/stable-diffusion-v1-5",
        subfolder="unet",
        torch_dtype=torch.float32
    )
    
    # Load student weights
    student_state_dict = checkpoint["student"]
    
    # Strip "unet." prefix if present
    cleaned_dict = {}
    for key, value in student_state_dict.items():
        if key.startswith("unet."):
            cleaned_dict[key[5:]] = value
        else:
            cleaned_dict[key] = value
    
    # Load weights
    unet.load_state_dict(cleaned_dict, strict=False)
    
    step = checkpoint.get("gstep", "unknown")
    print(f"✅ Loaded checkpoint from step {step}")
    
    return unet.to(device)


def load_lyra_vae(repo_id: str = "AbstractPhil/vae-lyra", device: str = "cuda"):
    """Load Lyra VAE from HuggingFace."""
    if not LYRA_AVAILABLE:
        print("⚠️ Lyra VAE not available - geovocab2 not installed")
        return None
    
    print(f"🎵 Loading Lyra VAE from {repo_id}...")
    
    try:
        # Download checkpoint
        checkpoint_path = hf_hub_download(
            repo_id=repo_id,
            filename="best_model.pt",
            repo_type="model"
        )
        
        print(f"✓ Downloaded checkpoint: {checkpoint_path}")
        
        # Load checkpoint
        checkpoint = torch.load(checkpoint_path, map_location="cpu")
        
        # Extract config
        if 'config' in checkpoint:
            config_dict = checkpoint['config']
        else:
            # Use default config
            config_dict = {
                'modality_dims': {"clip": 768, "t5": 768},
                'latent_dim': 768,
                'seq_len': 77,
                'encoder_layers': 3,
                'decoder_layers': 3,
                'hidden_dim': 1024,
                'dropout': 0.1,
                'fusion_strategy': 'cantor',
                'fusion_heads': 8,
                'fusion_dropout': 0.1
            }
        
        # Create VAE config
        vae_config = MultiModalVAEConfig(
            modality_dims=config_dict.get('modality_dims', {"clip": 768, "t5": 768}),
            latent_dim=config_dict.get('latent_dim', 768),
            seq_len=config_dict.get('seq_len', 77),
            encoder_layers=config_dict.get('encoder_layers', 3),
            decoder_layers=config_dict.get('decoder_layers', 3),
            hidden_dim=config_dict.get('hidden_dim', 1024),
            dropout=config_dict.get('dropout', 0.1),
            fusion_strategy=config_dict.get('fusion_strategy', 'cantor'),
            fusion_heads=config_dict.get('fusion_heads', 8),
            fusion_dropout=config_dict.get('fusion_dropout', 0.1)
        )
        
        # Create model
        lyra_model = MultiModalVAE(vae_config)
        
        # Load weights
        if 'model_state_dict' in checkpoint:
            lyra_model.load_state_dict(checkpoint['model_state_dict'])
        else:
            lyra_model.load_state_dict(checkpoint)
        
        lyra_model.to(device)
        lyra_model.eval()
        
        # Print info
        print(f"✅ Lyra VAE loaded successfully")
        if 'global_step' in checkpoint:
            print(f"   Training step: {checkpoint['global_step']:,}")
        if 'best_loss' in checkpoint:
            print(f"   Best loss: {checkpoint['best_loss']:.4f}")
        print(f"   Fusion strategy: {vae_config.fusion_strategy}")
        print(f"   Latent dim: {vae_config.latent_dim}")
        
        return lyra_model
        
    except Exception as e:
        print(f"❌ Failed to load Lyra VAE: {e}")
        return None


def initialize_pipeline(model_choice: str, device: str = "cuda"):
    """Initialize the complete pipeline."""
    
    print(f"🚀 Initializing {model_choice} pipeline...")
    
    is_lune = "Lune" in model_choice
    is_lyra = "Lyra" in model_choice
    
    # Load base components
    print("Loading VAE...")
    vae = AutoencoderKL.from_pretrained(
        "runwayml/stable-diffusion-v1-5",
        subfolder="vae",
        torch_dtype=torch.float32
    ).to(device)
    
    print("Loading CLIP text encoder...")
    text_encoder = CLIPTextModel.from_pretrained(
        "openai/clip-vit-large-patch14",
        torch_dtype=torch.float32
    ).to(device)
    
    tokenizer = CLIPTokenizer.from_pretrained(
        "openai/clip-vit-large-patch14"
    )
    
    # Load T5 and Lyra if needed
    t5_encoder = None
    t5_tokenizer = None
    lyra_model = None
    
    if is_lyra:
        print("Loading T5-base encoder...")
        t5_tokenizer = T5Tokenizer.from_pretrained("t5-base")
        t5_encoder = T5EncoderModel.from_pretrained(
            "t5-base",
            torch_dtype=torch.float32
        ).to(device)
        t5_encoder.eval()
        print("✓ T5 loaded")
        
        print("Loading Lyra VAE...")
        lyra_model = load_lyra_vae(device=device)
        if lyra_model is None:
            raise ValueError("Failed to load Lyra VAE")
    
    # Load UNet based on model choice
    if is_lune:
        # Load latest checkpoint from repo
        repo_id = "AbstractPhil/sd15-flow-lune"
        # Find latest checkpoint - for now use a known one
        filename = "sd15_flow_lune_e34_s34000.pt"
        unet = load_lune_checkpoint(repo_id, filename, device)
    
    elif is_lyra or model_choice == "SD1.5 Base":
        # Use standard SD1.5 UNet for both Lyra and base
        print("Loading SD1.5 base UNet...")
        unet = UNet2DConditionModel.from_pretrained(
            "runwayml/stable-diffusion-v1-5",
            subfolder="unet",
            torch_dtype=torch.float32
        ).to(device)
    
    else:
        raise ValueError(f"Unknown model: {model_choice}")
    
    # Initialize scheduler
    scheduler = EulerDiscreteScheduler.from_pretrained(
        "runwayml/stable-diffusion-v1-5",
        subfolder="scheduler"
    )
    
    print("✅ Pipeline initialized!")
    
    pipeline = FlowMatchingPipeline(
        vae=vae,
        text_encoder=text_encoder,
        tokenizer=tokenizer,
        unet=unet,
        scheduler=scheduler,
        device=device,
        t5_encoder=t5_encoder,
        t5_tokenizer=t5_tokenizer,
        lyra_model=lyra_model
    )
    
    # Set flag for Lune-specific VAE scaling
    pipeline.is_lune_model = is_lune
    
    return pipeline


# ============================================================================
# GLOBAL STATE
# ============================================================================

# Initialize with None, will load on first inference
CURRENT_PIPELINE = None
CURRENT_MODEL = None


def get_pipeline(model_choice: str):
    """Get or create pipeline for selected model."""
    global CURRENT_PIPELINE, CURRENT_MODEL
    
    if CURRENT_PIPELINE is None or CURRENT_MODEL != model_choice:
        CURRENT_PIPELINE = initialize_pipeline(model_choice, device="cuda")
        CURRENT_MODEL = model_choice
    
    return CURRENT_PIPELINE


# ============================================================================
# INFERENCE
# ============================================================================

def estimate_duration(num_steps: int, width: int, height: int) -> int:
    """Estimate GPU duration based on generation parameters."""
    # Base time per step (seconds)
    base_time_per_step = 0.3
    
    # Resolution scaling
    resolution_factor = (width * height) / (512 * 512)
    
    # Total estimate
    estimated = num_steps * base_time_per_step * resolution_factor
    
    # Add 15 seconds for model loading overhead
    return int(estimated + 15)


@spaces.GPU(duration=lambda *args: estimate_duration(args[3], args[5], args[6]))
def generate_image(
    prompt: str,
    negative_prompt: str,
    model_choice: str,
    num_steps: int,
    cfg_scale: float,
    width: int,
    height: int,
    shift: float,
    use_flow_matching: bool,
    prediction_type: str,
    seed: int,
    randomize_seed: bool,
    progress=gr.Progress()
):
    """Generate image with ZeroGPU support."""
    
    # Randomize seed if requested
    if randomize_seed:
        seed = np.random.randint(0, 2**32 - 1)
    
    # Progress tracking
    def progress_callback(step, total, desc):
        progress((step + 1) / total, desc=desc)
    
    try:
        # Get pipeline
        pipeline = get_pipeline(model_choice)
        
        # Determine if we should use Lyra encoding
        use_lyra = "Lyra" in model_choice
        
        # Generate
        progress(0.05, desc="Starting generation...")
        
        image = pipeline(
            prompt=prompt,
            negative_prompt=negative_prompt,
            height=height,
            width=width,
            num_inference_steps=num_steps,
            guidance_scale=cfg_scale,
            shift=shift,
            use_flow_matching=use_flow_matching,
            prediction_type=prediction_type,
            seed=seed,
            use_lyra=use_lyra,
            progress_callback=progress_callback
        )
        
        progress(1.0, desc="Complete!")
        
        return image, seed
    
    except Exception as e:
        print(f"❌ Generation failed: {e}")
        raise e


# ============================================================================
# GRADIO UI
# ============================================================================

def create_demo():
    """Create Gradio interface."""
    
    with gr.Blocks(theme=gr.themes.Soft()) as demo:
        gr.Markdown("""
        # 🌙 Lyra/Lune Flow-Matching Image Generation
        
        **Geometric crystalline diffusion with flow matching** by [AbstractPhil](https://huggingface.co/AbstractPhil)
        
        Generate images using SD1.5-based models with geometric deep learning approaches:
        - **Flow-Lune**: Flow matching with pentachoron geometric structures
        - **Lyra-VAE**: Multi-modal fusion (CLIP+T5) via geometric attention
        - **SD1.5 Base**: Standard baseline for comparison
        
        Achieves high quality with dramatically reduced step counts through geometric efficiency.
        """)
        
        with gr.Row():
            with gr.Column(scale=1):
                # Prompt - default to first example
                prompt = gr.TextArea(
                    label="Prompt",
                    value="A serene mountain landscape at golden hour, crystal clear lake reflecting snow-capped peaks, photorealistic, 8k",
                    lines=3
                )
                
                negative_prompt = gr.TextArea(
                    label="Negative Prompt",
                    placeholder="blurry, low quality, distorted...",
                    value="blurry, low quality",
                    lines=2
                )
                
                # Model selection
                model_choice = gr.Dropdown(
                    label="Model",
                    choices=[
                        "Flow-Lune (Latest)",
                        "Lyra-VAE (Geometric Fusion)",
                        "SD1.5 Base"
                    ],
                    value="Flow-Lune (Latest)"
                )
                
                # Flow matching settings
                with gr.Accordion("Flow Matching Settings", open=True):
                    use_flow_matching = gr.Checkbox(
                        label="Enable Flow Matching",
                        value=True,
                        info="Use flow matching ODE integration"
                    )
                    
                    shift = gr.Slider(
                        label="Shift",
                        minimum=0.0,
                        maximum=5.0,
                        value=2.5,
                        step=0.1,
                        info="Flow matching shift parameter (0=disabled, 1-3 typical)"
                    )
                    
                    prediction_type = gr.Radio(
                        label="Prediction Type",
                        choices=["epsilon", "v_prediction"],
                        value="v_prediction",  # Default to v_prediction for Lune
                        info="Type of model prediction"
                    )
                
                # Generation settings
                with gr.Accordion("Generation Settings", open=True):
                    num_steps = gr.Slider(
                        label="Steps",
                        minimum=1,
                        maximum=50,
                        value=20,
                        step=1,
                        info="Flow matching typically needs fewer steps (15-25)"
                    )
                    
                    cfg_scale = gr.Slider(
                        label="CFG Scale",
                        minimum=1.0,
                        maximum=20.0,
                        value=7.5,
                        step=0.5
                    )
                    
                    with gr.Row():
                        width = gr.Slider(
                            label="Width",
                            minimum=256,
                            maximum=1024,
                            value=512,
                            step=64
                        )
                        
                        height = gr.Slider(
                            label="Height",
                            minimum=256,
                            maximum=1024,
                            value=512,
                            step=64
                        )
                    
                    seed = gr.Slider(
                        label="Seed",
                        minimum=0,
                        maximum=2**32 - 1,
                        value=42,
                        step=1
                    )
                    
                    randomize_seed = gr.Checkbox(
                        label="Randomize Seed",
                        value=True
                    )
                
                generate_btn = gr.Button("🎨 Generate", variant="primary", size="lg")
            
            with gr.Column(scale=1):
                output_image = gr.Image(
                    label="Generated Image",
                    type="pil"
                )
                
                output_seed = gr.Number(
                    label="Used Seed",
                    precision=0
                )
                
                gr.Markdown("""
                ### Tips:
                - **Flow matching** works best with 15-25 steps (vs 50+ for standard diffusion)
                - **Shift** controls the flow trajectory (2.0-2.5 recommended for Lune)
                - Lower shift = more direct path, higher shift = more exploration
                - **Lune** uses v_prediction by default for optimal results
                - **Lyra** fuses CLIP+T5 encoders through geometric VAE for richer embeddings
                - **SD1.5 Base** uses epsilon (standard diffusion)
                - Lune operates in a scaled latent space (5.52x) for geometric efficiency
                
                ### Model Info:
                - **Flow-Lune**: Trained with flow matching on 500k SD1.5 distillation pairs
                - **Lyra-VAE**: Multi-modal fusion (CLIP+T5) via Cantor geometric attention
                - **SD1.5 Base**: Standard Stable Diffusion 1.5 for comparison
                
                [📚 Learn more about geometric deep learning](https://github.com/AbstractEyes/lattice_vocabulary)
                """)
        
        # Examples
        gr.Examples(
            examples=[
                [
                    "A serene mountain landscape at golden hour, crystal clear lake reflecting snow-capped peaks, photorealistic, 8k",
                    "blurry, low quality",
                    "Flow-Lune (Latest)",
                    20,
                    7.5,
                    512,
                    512,
                    2.5,
                    True,
                    "v_prediction",
                    42,
                    False
                ],
                [
                    "A futuristic cyberpunk city at night, neon lights, rain-slicked streets, highly detailed",
                    "low quality, blurry",
                    "Lyra-VAE (Geometric Fusion)",
                    30,
                    7.5,
                    512,
                    512,
                    0.0,
                    False,
                    "epsilon",
                    123,
                    False
                ],
                [
                    "Portrait of a majestic lion, golden mane, dramatic lighting, wildlife photography",
                    "cartoon, painting",
                    "Flow-Lune (Latest)",
                    18,
                    7.0,
                    512,
                    512,
                    2.0,
                    True,
                    "v_prediction",
                    456,
                    False
                ]
            ],
            inputs=[
                prompt, negative_prompt, model_choice, num_steps, cfg_scale,
                width, height, shift, use_flow_matching, prediction_type,
                seed, randomize_seed
            ],
            outputs=[output_image, output_seed],
            fn=generate_image,
            cache_examples=False
        )
        
        # Event handlers
        
        # Update settings when model changes
        def on_model_change(model_name):
            """Update default settings based on model selection."""
            if model_name == "SD1.5 Base":
                # SD1.5: disable flow matching, use epsilon
                return {
                    use_flow_matching: gr.update(value=False),
                    prediction_type: gr.update(value="epsilon")
                }
            elif model_name == "Lyra-VAE (Geometric Fusion)":
                # Lyra: disable flow matching (uses standard diffusion), use epsilon
                return {
                    use_flow_matching: gr.update(value=False),
                    prediction_type: gr.update(value="epsilon")
                }
            else:
                # Lune: enable flow matching, use v_prediction
                return {
                    use_flow_matching: gr.update(value=True),
                    prediction_type: gr.update(value="v_prediction")
                }
        
        model_choice.change(
            fn=on_model_change,
            inputs=[model_choice],
            outputs=[use_flow_matching, prediction_type]
        )
        
        generate_btn.click(
            fn=generate_image,
            inputs=[
                prompt, negative_prompt, model_choice, num_steps, cfg_scale,
                width, height, shift, use_flow_matching, prediction_type,
                seed, randomize_seed
            ],
            outputs=[output_image, output_seed]
        )
    
    return demo


# ============================================================================
# LAUNCH
# ============================================================================

if __name__ == "__main__":
    demo = create_demo()
    demo.queue(max_size=20)
    demo.launch(show_api=False)