Delete app.py

app.py

@@ -1,938 +0,0 @@
-# =============================================================================
-# Installation and Setup
-# =============================================================================
-import os
-import subprocess
-import sys
-
-os.environ["TORCH_COMPILE_DISABLE"] = "1"
-os.environ["TORCHDYNAMO_DISABLE"] = "1"
-
-subprocess.run([sys.executable, "-m", "pip", "install", "xformers==0.0.32.post2", "--no-build-isolation"], check=False)
-
-LTX_REPO_URL = "https://github.com/Lightricks/LTX-2.git"
-LTX_REPO_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "LTX-2")
-# This commit has TI2VidTwoStagesHQPipeline with ModelLedger
-LTX_COMMIT = "ae855f8538843825f9015a419cf4ba5edaf5eec2"
-
-if not os.path.exists(LTX_REPO_DIR):
-    print(f"Cloning {LTX_REPO_URL}...")
-    subprocess.run(["git", "clone", LTX_REPO_URL, LTX_REPO_DIR], check=True)
-    subprocess.run(["git", "checkout", LTX_COMMIT], cwd=LTX_REPO_DIR, check=True)
-
-print("Installing ltx-core and ltx-pipelines from cloned repo...")
-subprocess.run(
-    [sys.executable, "-m", "pip", "install", "--force-reinstall", "--no-deps", "-e",
-     os.path.join(LTX_REPO_DIR, "packages", "ltx-core"),
-     "-e", os.path.join(LTX_REPO_DIR, "packages", "ltx-pipelines")],
-    check=True,
-)
-
-sys.path.insert(0, os.path.join(LTX_REPO_DIR, "packages", "ltx-pipelines", "src"))
-sys.path.insert(0, os.path.join(LTX_REPO_DIR, "packages", "ltx-core", "src"))
-
-import logging
-import random
-import tempfile
-from pathlib import Path
-import gc
-import hashlib
-
-import torch
-torch._dynamo.config.suppress_errors = True
-torch._dynamo.config.disable = True
-
-import spaces
-import gradio as gr
-import numpy as np
-from huggingface_hub import hf_hub_download, snapshot_download
-from safetensors.torch import load_file, save_file
-
-from ltx_core.model.video_vae import TilingConfig, get_video_chunks_number
-from ltx_core.quantization import QuantizationPolicy
-from ltx_core.loader import LoraPathStrengthAndSDOps, LTXV_LORA_COMFY_RENAMING_MAP
-from ltx_core.components.guiders import MultiModalGuider, MultiModalGuiderParams
-from ltx_core.components.noisers import GaussianNoiser
-from ltx_core.components.diffusion_steps import Res2sDiffusionStep
-from ltx_core.components.schedulers import LTX2Scheduler
-from ltx_core.types import Audio, LatentState, VideoPixelShape
-from ltx_core.tools import VideoLatentShape
-from ltx_pipelines.ti2vid_two_stages_hq import TI2VidTwoStagesHQPipeline
-from ltx_pipelines.utils.args import ImageConditioningInput
-from ltx_pipelines.utils.constants import LTX_2_3_HQ_PARAMS, STAGE_2_DISTILLED_SIGMA_VALUES
-from ltx_pipelines.utils.media_io import encode_video
-from ltx_pipelines.utils.helpers import (
-    assert_resolution,
-    cleanup_memory,
-    combined_image_conditionings,
-    get_device,
-)
-
-# Patch xformers
-try:
-    from ltx_core.model.transformer import attention as _attn_mod
-    from xformers.ops import memory_efficient_attention as _mea
-    _attn_mod.memory_efficient_attention = _mea
-    print("[ATTN] xformers patch applied")
-except Exception as e:
-    print(f"[ATTN] xformers patch failed: {e}")
-
-logging.getLogger().setLevel(logging.INFO)
-
-MAX_SEED = np.iinfo(np.int32).max
-DEFAULT_PROMPT = (
-    "A majestic eagle soaring over mountain peaks at sunset, "
-    "wings spread wide against the orange sky, feathers catching the light, "
-    "wind currents visible in the motion blur, cinematic slow motion, 4K quality"
-)
-DEFAULT_NEGATIVE_PROMPT = (
-    "worst quality, inconsistent motion, blurry, jittery, distorted, "
-    "deformed, artifacts, text, watermark, logo, frame, border, "
-    "low resolution, pixelated, unnatural, fake, CGI, cartoon"
-)
-DEFAULT_FRAME_RATE = 24.0
-MIN_DIM, MAX_DIM, STEP = 256, 1280, 64
-MIN_FRAMES, MAX_FRAMES = 9, 257
-
-RESOLUTIONS = {
-    "16:9": {"width": 1280, "height": 704},
-    "9:16": {"width": 704, "height": 1280},
-    "1:1": {"width": 960, "height": 960},
-}
-
-LTX_MODEL_REPO = "Lightricks/LTX-2.3"
-GEMMA_REPO = "Lightricks/gemma-3-12b-it-qat-q4_0-unquantized"
-
-# =============================================================================
-# Custom HQ Pipeline with LoRA Cache Support
-# =============================================================================
-
-class HQPipelineWithCachedLoRA(TI2VidTwoStagesHQPipeline):
-    """
-    TI2VidTwoStagesHQPipeline with support for cached LoRA state.
-    Bypasses GPU LoRA fusion by applying pre-cached state_dict.
-    Supports negative prompts and guidance parameters.
-    """
-    
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        # Storage for cached LoRA states for each stage
-        self._cached_state_stage1 = None
-        self._cached_state_stage2 = None
-    
-    def apply_cached_lora_state(self, state_dict_stage1, state_dict_stage2=None):
-        """Apply pre-cached LoRA state to both stage transformers."""
-        self._cached_state_stage1 = state_dict_stage1
-        self._cached_state_stage2 = state_dict_stage2 if state_dict_stage2 else state_dict_stage1
-    
-    @torch.inference_mode()
-    def __call__(  # noqa: PLR0913
-        self,
-        prompt: str,
-        negative_prompt: str,
-        seed: int,
-        height: int,
-        width: int,
-        num_frames: int,
-        frame_rate: float,
-        num_inference_steps: int,
-        video_guider_params: MultiModalGuiderParams,
-        audio_guider_params: MultiModalGuiderParams,
-        images: list,
-        tiling_config: TilingConfig | None = None,
-        enhance_prompt: bool = False,
-    ):
-        """
-        Generate video with cached LoRA state and CFG guidance.
-        """
-        assert_resolution(height=height, width=width, is_two_stage=True)
-        
-        device = self.device
-        dtype = self.dtype
-        generator = torch.Generator(device=device).manual_seed(seed)
-        noiser = GaussianNoiser(generator=generator)
-        
-        # Apply cached LoRA state if available
-        if self._cached_state_stage1 is not None:
-            print("[LoRA] Applying cached state to stage 1 transformer...")
-            t1 = self.stage_1_model_ledger.transformer()
-            with torch.no_grad():
-                t1.load_state_dict(self._cached_state_stage1, strict=False)
-        
-        if self._cached_state_stage2 is not None:
-            print("[LoRA] Applying cached state to stage 2 transformer...")
-            t2 = self.stage_2_model_ledger.transformer()
-            with torch.no_grad():
-                t2.load_state_dict(self._cached_state_stage2, strict=False)
-        
-        # Encode prompts
-        from ltx_pipelines.utils.helpers import encode_prompts
-        ctx_p, ctx_n = encode_prompts(
-            [prompt, negative_prompt],
-            self.stage_1_model_ledger,
-            enhance_first_prompt=enhance_prompt,
-            enhance_prompt_image=images[0][0] if len(images) > 0 else None,
-            enhance_prompt_seed=seed,
-        )
-        
-        v_context_p, a_context_p = ctx_p.video_encoding, ctx_p.audio_encoding
-        v_context_n, a_context_n = ctx_n.video_encoding, ctx_n.audio_encoding
-        
-        # Stage 1
-        stage_1_output_shape = VideoPixelShape(
-            batch=1, frames=num_frames,
-            width=width // 2, height=height // 2, fps=frame_rate
-        )
-        
-        video_encoder = self.stage_1_model_ledger.video_encoder()
-        stage_1_conditionings = combined_image_conditionings(
-            images=images,
-            height=stage_1_output_shape.height,
-            width=stage_1_output_shape.width,
-            video_encoder=video_encoder,
-            dtype=dtype,
-            device=device,
-        )
-        torch.cuda.synchronize()
-        del video_encoder
-        cleanup_memory()
-        
-        transformer = self.stage_1_model_ledger.transformer()
-        
-        empty_latent = torch.empty(VideoLatentShape.from_pixel_shape(stage_1_output_shape).to_torch_shape())
-        stepper = Res2sDiffusionStep()
-        sigmas = (
-            LTX2Scheduler()
-            .execute(latent=empty_latent, steps=num_inference_steps)
-            .to(dtype=torch.float32, device=device)
-        )
-        
-        # Stage 1 denoising with CFG
-        from ltx_pipelines.utils.helpers import multi_modal_guider_denoising_func, res2s_audio_video_denoising_loop
-        
-        def first_stage_denoising_loop(sigmas, video_state, audio_state, stepper):
-            return res2s_audio_video_denoising_loop(
-                sigmas=sigmas,
-                video_state=video_state,
-                audio_state=audio_state,
-                stepper=stepper,
-                denoise_fn=multi_modal_guider_denoising_func(
-                    video_guider=MultiModalGuider(params=video_guider_params, negative_context=v_context_n),
-                    audio_guider=MultiModalGuider(params=audio_guider_params, negative_context=a_context_n),
-                    v_context=v_context_p,
-                    a_context=a_context_p,
-                    transformer=transformer,
-                ),
-            )
-        
-        from ltx_pipelines.utils.helpers import denoise_audio_video
-        video_state, audio_state = denoise_audio_video(
-            output_shape=stage_1_output_shape,
-            conditionings=stage_1_conditionings,
-            noiser=noiser,
-            sigmas=sigmas,
-            stepper=stepper,
-            denoising_loop_fn=first_stage_denoising_loop,
-            components=self.pipeline_components,
-            dtype=dtype,
-            device=device,
-        )
-        
-        torch.cuda.synchronize()
-        del transformer
-        cleanup_memory()
-        
-        # Stage 2: Upsample and refine
-        from ltx_core.model.upsampler import upsample_video
-        video_encoder = self.stage_1_model_ledger.video_encoder()
-        upscaled_video_latent = upsample_video(
-            latent=video_state.latent[:1],
-            video_encoder=video_encoder,
-            upsampler=self.stage_2_model_ledger.spatial_upsampler(),
-        )
-        
-        stage_2_output_shape = VideoPixelShape(batch=1, frames=num_frames, width=width, height=height, fps=frame_rate)
-        stage_2_conditionings = combined_image_conditionings(
-            images=images,
-            height=stage_2_output_shape.height,
-            width=stage_2_output_shape.width,
-            video_encoder=video_encoder,
-            dtype=dtype,
-            device=device,
-        )
-        torch.cuda.synchronize()
-        del video_encoder
-        cleanup_memory()
-        
-        transformer = self.stage_2_model_ledger.transformer()
-        distilled_sigmas = torch.tensor(STAGE_2_DISTILLED_SIGMA_VALUES, device=device)
-        
-        from ltx_pipelines.utils.helpers import simple_denoising_func
-        
-        def second_stage_denoising_loop(sigmas, video_state, audio_state, stepper):
-            return res2s_audio_video_denoising_loop(
-                sigmas=sigmas,
-                video_state=video_state,
-                audio_state=audio_state,
-                stepper=stepper,
-                denoise_fn=simple_denoising_func(
-                    video_context=v_context_p,
-                    audio_context=a_context_p,
-                    transformer=transformer,
-                ),
-            )
-        
-        video_state, audio_state = denoise_audio_video(
-            output_shape=stage_2_output_shape,
-            conditionings=stage_2_conditionings,
-            noiser=noiser,
-            sigmas=distilled_sigmas,
-            stepper=stepper,
-            denoising_loop_fn=second_stage_denoising_loop,
-            components=self.pipeline_components,
-            dtype=dtype,
-            device=device,
-            noise_scale=distilled_sigmas[0],
-            initial_video_latent=upscaled_video_latent,
-            initial_audio_latent=audio_state.latent,
-        )
-        
-        torch.cuda.synchronize()
-        del transformer
-        cleanup_memory()
-        
-        # Decode
-        from ltx_core.model.audio_vae import decode_audio as vae_decode_audio
-        from ltx_core.model.video_vae import decode_video as vae_decode_video
-        
-        decoded_video = vae_decode_video(
-            video_state.latent, self.stage_2_model_ledger.video_decoder(), tiling_config, generator
-        )
-        decoded_audio = vae_decode_audio(
-            audio_state.latent, self.stage_2_model_ledger.audio_decoder(), self.stage_2_model_ledger.vocoder()
-        )
-        
-        return decoded_video, decoded_audio
-
-
-# =============================================================================
-# Model Download
-# =============================================================================
-
-print("=" * 80)
-print("Downloading LTX-2.3 HQ models...")
-print("=" * 80)
-
-checkpoint_path = hf_hub_download(repo_id=LTX_MODEL_REPO, filename="ltx-2.3-22b-dev.safetensors")
-spatial_upsampler_path = hf_hub_download(repo_id=LTX_MODEL_REPO, filename="ltx-2.3-spatial-upscaler-x2-1.1.safetensors")
-distilled_lora_path = hf_hub_download(repo_id=LTX_MODEL_REPO, filename="ltx-2.3-22b-distilled-lora-384.safetensors")
-gemma_root = snapshot_download(repo_id=GEMMA_REPO)
-
-print(f"Dev checkpoint: {checkpoint_path}")
-print(f"Spatial upsampler: {spatial_upsampler_path}")
-print(f"Distilled LoRA: {distilled_lora_path}")
-print(f"Gemma root: {gemma_root}")
-
-# =============================================================================
-# Download Custom LoRAs (from your existing app.py)
-# =============================================================================
-
-LORA_REPO = "dagloop5/LoRA"
-
-print("=" * 80)
-print("Downloading custom LoRA adapters...")
-print("=" * 80)
-
-pose_lora_path = hf_hub_download(repo_id=LORA_REPO, filename="LTX2_3_NSFW_furry_concat_v2.safetensors")
-general_lora_path = hf_hub_download(repo_id=LORA_REPO, filename="LTX2.3_reasoning_I2V_V3.safetensors")
-motion_lora_path = hf_hub_download(repo_id=LORA_REPO, filename="motion_helper.safetensors")
-dreamlay_lora_path = hf_hub_download(repo_id=LORA_REPO, filename="DR34ML4Y_LTXXX_PREVIEW_RC1.safetensors")
-mself_lora_path = hf_hub_download(repo_id=LORA_REPO, filename="Furry Hyper Masturbation - LTX-2 I2V v1.safetensors")
-dramatic_lora_path = hf_hub_download(repo_id=LORA_REPO, filename="LTX-2.3 - Orgasm.safetensors")
-fluid_lora_path = hf_hub_download(repo_id=LORA_REPO, filename="cr3ampi3_animation_i2v_ltx2_v1.0.safetensors")
-liquid_lora_path = hf_hub_download(repo_id=LORA_REPO, filename="liquid_wet_dr1pp_ltx2_v1.0_scaled.safetensors")
-demopose_lora_path = hf_hub_download(repo_id=LORA_REPO, filename="clapping-cheeks-audio-v001-alpha.safetensors")
-voice_lora_path = hf_hub_download(repo_id=LORA_REPO, filename="hentai_voice_ltx23.safetensors")
-realism_lora_path = hf_hub_download(repo_id=LORA_REPO, filename="FurryenhancerLTX2.3V1.215.safetensors")
-transition_lora_path = hf_hub_download(repo_id=LORA_REPO, filename="LTX-2_takerpov_lora_v1.2.safetensors")
-
-print(f"All {12} custom LoRAs downloaded")
-print("=" * 80)
-
-# =============================================================================
-# Pipeline Initialization (with empty loras - LoRAs applied via cache)
-# =============================================================================
-
-print("Initializing HQ Pipeline with LoRA cache support...")
-
-distilled_lora = [
-    LoraPathStrengthAndSDOps(
-        path=distilled_lora_path,
-        strength=1.0,
-        sd_ops=LTXV_LORA_COMFY_RENAMING_MAP,
-    )
-]
-
-pipeline = HQPipelineWithCachedLoRA(
-    checkpoint_path=checkpoint_path,
-    distilled_lora=distilled_lora,
-    distilled_lora_strength_stage_1=0.25,
-    distilled_lora_strength_stage_2=0.50,
-    spatial_upsampler_path=spatial_upsampler_path,
-    gemma_root=gemma_root,
-    loras=(),  # LoRAs will be applied via cached state
-    quantization=QuantizationPolicy.fp8_cast(),
-)
-
-print("Pipeline initialized!")
-print("=" * 80)
-
-# =============================================================================
-# ZeroGPU Tensor Preloading
-# =============================================================================
-
-print("Preloading all models for ZeroGPU tensor packing...")
-
-# Preload stage 1 models
-ledger1 = pipeline.stage_1_model_ledger
-_transformer_s1 = ledger1.transformer()
-ledger1.transformer = lambda: _transformer_s1
-
-_video_encoder_s1 = ledger1.video_encoder()
-ledger1.video_encoder = lambda: _video_encoder_s1
-
-_video_decoder_s1 = ledger1.video_decoder()
-ledger1.video_decoder = lambda: _video_decoder_s1
-
-_audio_decoder_s1 = ledger1.audio_decoder()
-ledger1.audio_decoder = lambda: _audio_decoder_s1
-
-_vocoder_s1 = ledger1.vocoder()
-ledger1.vocoder = lambda: _vocoder_s1
-
-_spatial_upsampler_s1 = ledger1.spatial_upsampler()
-ledger1.spatial_upsampler = lambda: _spatial_upsampler_s1
-
-_text_encoder_s1 = ledger1.text_encoder()
-ledger1.text_encoder = lambda: _text_encoder_s1
-
-_embeddings_processor_s1 = ledger1.embeddings_processor()
-ledger1.embeddings_processor = lambda: _embeddings_processor_s1
-
-print("  Stage 1 models preloaded")
-
-# Preload stage 2 models
-ledger2 = pipeline.stage_2_model_ledger
-_transformer_s2 = ledger2.transformer()
-ledger2.transformer = lambda: _transformer_s2
-
-_video_encoder_s2 = ledger2.video_encoder()
-ledger2.video_encoder = lambda: _video_encoder_s2
-
-_video_decoder_s2 = ledger2.video_decoder()
-ledger2.video_decoder = lambda: _video_decoder_s2
-
-_audio_decoder_s2 = ledger2.audio_decoder()
-ledger2.audio_decoder = lambda: _audio_decoder_s2
-
-_vocoder_s2 = ledger2.vocoder()
-ledger2.vocoder = lambda: _vocoder_s2
-
-_spatial_upsampler_s2 = ledger2.spatial_upsampler()
-ledger2.spatial_upsampler = lambda: _spatial_upsampler_s2
-
-_text_encoder_s2 = ledger2.text_encoder()
-ledger2.text_encoder = lambda: _text_encoder_s2
-
-_embeddings_processor_s2 = ledger2.embeddings_processor()
-ledger2.embeddings_processor = lambda: _embeddings_processor_s2
-
-print("  Stage 2 models preloaded")
-
-print("All models preloaded for ZeroGPU tensor packing!")
-print("=" * 80)
-
-# =============================================================================
-# LoRA Cache Functions (from your existing app.py)
-# =============================================================================
-
-LORA_CACHE_DIR = Path("lora_cache")
-LORA_CACHE_DIR.mkdir(exist_ok=True)
-
-def prepare_lora_cache(
-    pose_strength: float, general_strength: float, motion_strength: float,
-    dreamlay_strength: float, mself_strength: float, dramatic_strength: float,
-    fluid_strength: float, liquid_strength: float, demopose_strength: float,
-    voice_strength: float, realism_strength: float, transition_strength: float,
-    progress=gr.Progress(track_tqdm=True),
-):
-    """
-    Build cached LoRA state for both stages (different strengths).
-    """
-    global _pipeline
-    
-    progress(0.05, desc="Preparing LoRA cache...")
-    
-    # Create key for cache
-    key_str = f"{checkpoint_path}:{pose_strength}:{general_strength}:{motion_strength}:{dreamlay_strength}:{mself_strength}:{dramatic_strength}:{fluid_strength}:{liquid_strength}:{demopose_strength}:{voice_strength}:{realism_strength}:{transition_strength}"
-    key = hashlib.sha256(key_str.encode()).hexdigest()
-    
-    cache_path_stage1 = LORA_CACHE_DIR / f"{key}_stage1.safetensors"
-    cache_path_stage2 = LORA_CACHE_DIR / f"{key}_stage2.safetensors"
-    
-    # Check if cached
-    if cache_path_stage1.exists() and cache_path_stage2.exists():
-        progress(0.20, desc="Loading cached LoRA state...")
-        state_stage1 = load_file(str(cache_path_stage1))
-        state_stage2 = load_file(str(cache_path_stage2))
-        pipeline.apply_cached_lora_state(state_stage1, state_stage2)
-        return f"Loaded cached LoRA state: {cache_path_stage1.name}"
-    
-    # Build LoRA list (distilled + 12 custom)
-    entries = [
-        (distilled_lora_path, 0.25),  # Stage 1 strength
-        (pose_lora_path, pose_strength),
-        (general_lora_path, general_strength),
-        (motion_lora_path, motion_strength),
-        (dreamlay_lora_path, dreamlay_strength),
-        (mself_lora_path, mself_strength),
-        (dramatic_lora_path, dramatic_strength),
-        (fluid_lora_path, fluid_strength),
-        (liquid_lora_path, liquid_strength),
-        (demopose_lora_path, demopose_strength),
-        (voice_lora_path, voice_strength),
-        (realism_lora_path, realism_strength),
-        (transition_lora_path, transition_strength),
-    ]
-    
-    loras = [
-        LoraPathStrengthAndSDOps(path, strength, LTXV_LORA_COMFY_RENAMING_MAP)
-        for path, strength in entries
-        if path is not None and float(strength) != 0.0
-    ]
-    
-    # Build stage 1 cached state
-    progress(0.35, desc="Building stage 1 fused state (CPU)...")
-    tmp_ledger1 = pipeline.stage_1_model_ledger.__class__(
-        dtype=torch.bfloat16,
-        device=torch.device("cpu"),
-        checkpoint_path=str(checkpoint_path),
-        spatial_upsampler_path=str(spatial_upsampler_path),
-        gemma_root_path=str(gemma_root),
-        loras=tuple(loras),
-        quantization=None,  # No quantization for CPU cache build
-    )
-    transformer1 = tmp_ledger1.transformer()
-    state_stage1 = {k: v.detach().cpu().contiguous() for k, v in transformer1.state_dict().items()}
-    save_file(state_stage1, str(cache_path_stage1))
-    
-    del transformer1, tmp_ledger1
-    gc.collect()
-    
-    # Build stage 2 cached state (different LoRA strengths)
-    progress(0.65, desc="Building stage 2 fused state (CPU)...")
-    
-    # Update strengths for stage 2
-    entries_s2 = [
-        (distilled_lora_path, 0.50),  # Stage 2 strength
-        (pose_lora_path, pose_strength),
-        (general_lora_path, general_strength),
-        (motion_lora_path, motion_strength),
-        (dreamlay_lora_path, dreamlay_strength),
-        (mself_lora_path, mself_strength),
-        (dramatic_lora_path, dramatic_strength),
-        (fluid_lora_path, fluid_strength),
-        (liquid_lora_path, liquid_strength),
-        (demopose_lora_path, demopose_strength),
-        (voice_lora_path, voice_strength),
-        (realism_lora_path, realism_strength),
-        (transition_lora_path, transition_strength),
-    ]
-    
-    loras_s2 = [
-        LoraPathStrengthAndSDOps(path, strength, LTXV_LORA_COMFY_RENAMING_MAP)
-        for path, strength in entries_s2
-        if path is not None and float(strength) != 0.0
-    ]
-    
-    tmp_ledger2 = pipeline.stage_2_model_ledger.__class__(
-        dtype=torch.bfloat16,
-        device=torch.device("cpu"),
-        checkpoint_path=str(checkpoint_path),
-        spatial_upsampler_path=str(spatial_upsampler_path),
-        gemma_root_path=str(gemma_root),
-        loras=tuple(loras_s2),
-        quantization=None,
-    )
-    transformer2 = tmp_ledger2.transformer()
-    state_stage2 = {k: v.detach().cpu().contiguous() for k, v in transformer2.state_dict().items()}
-    save_file(state_stage2, str(cache_path_stage2))
-    
-    del transformer2, tmp_ledger2
-    gc.collect()
-    
-    progress(0.90, desc="Applying LoRA state to pipeline...")
-    pipeline.apply_cached_lora_state(state_stage1, state_stage2)
-    
-    progress(1.0, desc="Done!")
-    return f"Built and cached LoRA state: {cache_path_stage1.name}"
-
-
-# =============================================================================
-# Helper Functions
-# =============================================================================
-
-def log_memory(tag: str):
-    if torch.cuda.is_available():
-        allocated = torch.cuda.memory_allocated() / 1024**3
-        peak = torch.cuda.max_memory_allocated() / 1024**3
-        free, total = torch.cuda.mem_get_info()
-        print(f"[VRAM {tag}] allocated={allocated:.2f}GB peak={peak:.2f}GB free={free / 1024**3:.2f}GB total={total / 1024**3:.2f}GB")
-
-
-def calculate_frames(duration: float, frame_rate: float = DEFAULT_FRAME_RATE) -> int:
-    ideal_frames = int(duration * frame_rate)
-    ideal_frames = max(ideal_frames, MIN_FRAMES)
-    k = round((ideal_frames - 1) / 8)
-    frames = k * 8 + 1
-    return min(frames, MAX_FRAMES)
-
-
-def validate_resolution(height: int, width: int) -> tuple[int, int]:
-    height = round(height / STEP) * STEP
-    width = round(width / STEP) * STEP
-    height = max(MIN_DIM, min(height, MAX_DIM))
-    width = max(MIN_DIM, min(width, MAX_DIM))
-    return height, width
-
-
-def detect_aspect_ratio(image) -> str:
-    if image is None:
-        return "16:9"
-    if hasattr(image, "size"):
-        w, h = image.size
-    elif hasattr(image, "shape"):
-        h, w = image.shape[:2]
-    else:
-        return "16:9"
-    ratio = w / h
-    candidates = {"16:9": 16/9, "9:16": 9/16, "1:1": 1.0}
-    return min(candidates, key=lambda k: abs(ratio - candidates[k]))
-
-
-def get_duration(
-    prompt, negative_prompt, first_image, last_image, input_audio,
-    duration, seed, randomize_seed, height, width, enhance_prompt,
-    video_cfg_scale, video_stg_scale, video_rescale_scale, video_a2v_scale,
-    audio_cfg_scale, audio_stg_scale, audio_rescale_scale, audio_v2a_scale,
-    pose_strength, general_strength, motion_strength, dreamlay_strength,
-    mself_strength, dramatic_strength, fluid_strength, liquid_strength,
-    demopose_strength, voice_strength, realism_strength, transition_strength,
-    progress=None,
-) -> int:
-    base = 90
-    if duration > 4:
-        base += 15
-    if duration > 6:
-        base += 15
-    if height > 700 or width > 1000:
-        base += 15
-    return min(base, 120)
-
-
-@spaces.GPU(duration=get_duration)
-@torch.inference_mode()
-def generate_video(
-    prompt: str,
-    negative_prompt: str,
-    first_image,
-    last_image,
-    input_audio,
-    duration: float,
-    seed: int,
-    randomize_seed: bool,
-    height: int,
-    width: int,
-    enhance_prompt: bool,
-    video_cfg_scale: float,
-    video_stg_scale: float,
-    video_rescale_scale: float,
-    video_a2v_scale: float,
-    audio_cfg_scale: float,
-    audio_stg_scale: float,
-    audio_rescale_scale: float,
-    audio_v2a_scale: float,
-    pose_strength: float,
-    general_strength: float,
-    motion_strength: float,
-    dreamlay_strength: float,
-    mself_strength: float,
-    dramatic_strength: float,
-    fluid_strength: float,
-    liquid_strength: float,
-    demopose_strength: float,
-    voice_strength: float,
-    realism_strength: float,
-    transition_strength: float,
-    progress=gr.Progress(track_tqdm=True),
-):
-    try:
-        torch.cuda.reset_peak_memory_stats()
-        log_memory("start")
-
-        current_seed = random.randint(0, MAX_SEED) if randomize_seed else int(seed)
-        print(f"Using seed: {current_seed}")
-
-        height, width = validate_resolution(int(height), int(width))
-        print(f"Resolution: {width}x{height}")
-
-        num_frames = calculate_frames(duration, DEFAULT_FRAME_RATE)
-        print(f"Frames: {num_frames} ({duration}s @ {DEFAULT_FRAME_RATE}fps)")
-
-        images = []
-        output_dir = Path("outputs")
-        output_dir.mkdir(exist_ok=True)
-
-        if first_image is not None:
-            temp_first_path = output_dir / f"temp_first_{current_seed}.jpg"
-            if hasattr(first_image, "save"):
-                first_image.save(temp_first_path)
-            else:
-                import shutil
-                shutil.copy(first_image, temp_first_path)
-            images.append((str(temp_first_path), 1.0))
-
-        if last_image is not None:
-            temp_last_path = output_dir / f"temp_last_{current_seed}.jpg"
-            if hasattr(last_image, "save"):
-                last_image.save(temp_last_path)
-            else:
-                import shutil
-                shutil.copy(last_image, temp_last_path)
-            images.append((str(temp_last_path), 1.0))
-
-        tiling_config = TilingConfig.default()
-        video_chunks_number = get_video_chunks_number(num_frames, tiling_config)
-
-        video_guider_params = MultiModalGuiderParams(
-            cfg_scale=video_cfg_scale,
-            stg_scale=video_stg_scale,
-            rescale_scale=video_rescale_scale,
-            modality_scale=video_a2v_scale,
-            skip_step=0,
-            stg_blocks=[],
-        )
-
-        audio_guider_params = MultiModalGuiderParams(
-            cfg_scale=audio_cfg_scale,
-            stg_scale=audio_stg_scale,
-            rescale_scale=audio_rescale_scale,
-            modality_scale=audio_v2a_scale,
-            skip_step=0,
-            stg_blocks=[],
-        )
-
-        log_memory("before pipeline call")
-
-        video, audio = pipeline(
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            seed=current_seed,
-            height=height,
-            width=width,
-            num_frames=num_frames,
-            frame_rate=DEFAULT_FRAME_RATE,
-            num_inference_steps=LTX_2_3_HQ_PARAMS.num_inference_steps,
-            video_guider_params=video_guider_params,
-            audio_guider_params=audio_guider_params,
-            images=images,
-            tiling_config=tiling_config,
-            enhance_prompt=enhance_prompt,
-        )
-
-        log_memory("after pipeline call")
-
-        output_path = tempfile.mktemp(suffix=".mp4")
-        encode_video(
-            video=video,
-            fps=DEFAULT_FRAME_RATE,
-            audio=audio,
-            output_path=output_path,
-            video_chunks_number=video_chunks_number,
-        )
-
-        log_memory("after encode_video")
-        return str(output_path), current_seed
-
-    except Exception as e:
-        import traceback
-        log_memory("on error")
-        print(f"Error: {str(e)}\n{traceback.format_exc()}")
-        return None, current_seed
-
-
-# =============================================================================
-# Gradio UI (Merged from your app.py)
-# =============================================================================
-
-css = """
-.fillable {max-width: 1200px !important}
-.progress-text {color: white}
-"""
-
-with gr.Blocks(title="LTX-2.3 Two-Stage HQ with LoRA Cache") as demo:
-    gr.Markdown("# LTX-2.3 Two-Stage HQ Video Generation with LoRA Cache")
-    gr.Markdown(
-        "High-quality text/image-to-video with cached LoRA state + CFG guidance. "
-        "[[Model]](https://huggingface.co/Lightricks/LTX-2.3)"
-    )
-
-    with gr.Row():
-        with gr.Column():
-            with gr.Row():
-                first_image = gr.Image(label="First Frame (Optional)", type="pil")
-                last_image = gr.Image(label="Last Frame (Optional)", type="pil")
-            
-            prompt = gr.Textbox(
-                label="Prompt",
-                value=DEFAULT_PROMPT,
-                lines=3,
-            )
-            
-            negative_prompt = gr.Textbox(
-                label="Negative Prompt",
-                value=DEFAULT_NEGATIVE_PROMPT,
-                lines=2,
-            )
-            
-            duration = gr.Slider(
-                label="Duration (seconds)",
-                minimum=0.5, maximum=8.0, value=2.0, step=0.1,
-            )
-            
-            enhance_prompt = gr.Checkbox(label="Enhance Prompt", value=False)
-
-            generate_btn = gr.Button("Generate Video", variant="primary", size="lg")
-
-        with gr.Column():
-            output_video = gr.Video(label="Generated Video", autoplay=True)
-
-    with gr.Accordion("Advanced Settings", open=False):
-        with gr.Row():
-            width = gr.Number(label="Width", value=1280, precision=0)
-            height = gr.Number(label="Height", value=704, precision=0)
-        
-        with gr.Row():
-            seed = gr.Number(label="Seed", value=42, precision=0, minimum=0, maximum=MAX_SEED)
-            randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
-        
-        gr.Markdown("### Video Guidance Parameters")
-        
-        with gr.Row():
-            video_cfg_scale = gr.Slider(
-                label="Video CFG Scale", minimum=1.0, maximum=10.0,
-                value=LTX_2_3_HQ_PARAMS.video_guider_params.cfg_scale, step=0.1
-            )
-            video_stg_scale = gr.Slider(
-                label="Video STG Scale", minimum=0.0, maximum=2.0, value=0.0, step=0.1
-            )
-        
-        with gr.Row():
-            video_rescale_scale = gr.Slider(
-                label="Video Rescale", minimum=0.0, maximum=2.0, value=0.45, step=0.1
-            )
-            video_a2v_scale = gr.Slider(
-                label="A2V Scale", minimum=0.0, maximum=5.0, value=3.0, step=0.1
-            )
-        
-        gr.Markdown("### Audio Guidance Parameters")
-        
-        with gr.Row():
-            audio_cfg_scale = gr.Slider(
-                label="Audio CFG Scale", minimum=1.0, maximum=15.0,
-                value=LTX_2_3_HQ_PARAMS.audio_guider_params.cfg_scale, step=0.1
-            )
-            audio_stg_scale = gr.Slider(
-                label="Audio STG Scale", minimum=0.0, maximum=2.0, value=0.0, step=0.1
-            )
-        
-        with gr.Row():
-            audio_rescale_scale = gr.Slider(
-                label="Audio Rescale", minimum=0.0, maximum=2.0, value=1.0, step=0.1
-            )
-            audio_v2a_scale = gr.Slider(
-                label="V2A Scale", minimum=0.0, maximum=5.0, value=3.0, step=0.1
-            )
-        
-        gr.Markdown("### LoRA Adapter Strengths")
-        
-        with gr.Row():
-            pose_strength = gr.Slider(label="Anthro Enhancer", minimum=0.0, maximum=2.0, value=0.0, step=0.01)
-            general_strength = gr.Slider(label="Reasoning Enhancer", minimum=0.0, maximum=2.0, value=0.0, step=0.01)
-        
-        with gr.Row():
-            motion_strength = gr.Slider(label="Anthro Posing", minimum=0.0, maximum=2.0, value=0.0, step=0.01)
-            dreamlay_strength = gr.Slider(label="Dreamlay", minimum=0.0, maximum=2.0, value=0.0, step=0.01)
-        
-        with gr.Row():
-            mself_strength = gr.Slider(label="Mself", minimum=0.0, maximum=2.0, value=0.0, step=0.01)
-            dramatic_strength = gr.Slider(label="Dramatic", minimum=0.0, maximum=2.0, value=0.0, step=0.01)
-        
-        with gr.Row():
-            fluid_strength = gr.Slider(label="Fluid Helper", minimum=0.0, maximum=2.0, value=0.0, step=0.01)
-            liquid_strength = gr.Slider(label="Liquid Helper", minimum=0.0, maximum=2.0, value=0.0, step=0.01)
-        
-        with gr.Row():
-            demopose_strength = gr.Slider(label="Audio Helper", minimum=0.0, maximum=2.0, value=0.0, step=0.01)
-            voice_strength = gr.Slider(label="Voice Helper", minimum=0.0, maximum=2.0, value=0.0, step=0.01)
-        
-        with gr.Row():
-            realism_strength = gr.Slider(label="Anthro Realism", minimum=0.0, maximum=2.0, value=0.0, step=0.01)
-            transition_strength = gr.Slider(label="POV", minimum=0.0, maximum=2.0, value=0.0, step=0.01)
-        
-        prepare_lora_btn = gr.Button("Prepare / Load LoRA Cache", variant="secondary")
-        lora_status = gr.Textbox(
-            label="LoRA Cache Status",
-            value="No LoRA state prepared yet.",
-            interactive=False,
-        )
-
-    def on_image_upload(image, current_h, current_w):
-        if image is None:
-            return gr.update(), gr.update()
-        aspect = detect_aspect_ratio(image)
-        if aspect in RESOLUTIONS:
-            return (
-                gr.update(value=RESOLUTIONS[aspect]["width"]),
-                gr.update(value=RESOLUTIONS[aspect]["height"])
-            )
-        return gr.update(), gr.update()
-
-    first_image.change(fn=on_image_upload, inputs=[first_image, height, width], outputs=[width, height])
-    last_image.change(fn=on_image_upload, inputs=[last_image, height, width], outputs=[width, height])
-
-    prepare_lora_btn.click(
-        fn=prepare_lora_cache,
-        inputs=[pose_strength, general_strength, motion_strength, dreamlay_strength,
-                mself_strength, dramatic_strength, fluid_strength, liquid_strength,
-                demopose_strength, voice_strength, realism_strength, transition_strength],
-        outputs=[lora_status],
-    )
-
-    generate_btn.click(
-        fn=generate_video,
-        inputs=[
-            prompt, negative_prompt, first_image, last_image, None, duration,
-            seed, randomize_seed, height, width, enhance_prompt,
-            video_cfg_scale, video_stg_scale, video_rescale_scale, video_a2v_scale,
-            audio_cfg_scale, audio_stg_scale, audio_rescale_scale, audio_v2a_scale,
-            pose_strength, general_strength, motion_strength, dreamlay_strength,
-            mself_strength, dramatic_strength, fluid_strength, liquid_strength,
-            demopose_strength, voice_strength, realism_strength, transition_strength,
-        ],
-        outputs=[output_video, seed],
-    )
-
-
-if __name__ == "__main__":
-    demo.queue().launch(theme=gr.themes.Citrus(), css=css, mcp_server=True)