Delete app.py

app.py

@@ -1,978 +0,0 @@
-import os
-import subprocess
-import sys
-
-# Disable torch.compile / dynamo before any torch import
-os.environ["TORCH_COMPILE_DISABLE"] = "1"
-os.environ["TORCHDYNAMO_DISABLE"] = "1"
-
-# Clone LTX-2 repo and install packages
-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")
-
-LTX_COMMIT = "ae855f8538843825f9015a419cf4ba5edaf5eec2"  # known working commit with decode_video
-
-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 safetensors import safe_open
-import json
-import requests
-
-from ltx_core.components.diffusion_steps import EulerDiffusionStep
-from ltx_core.components.guiders import MultiModalGuider, MultiModalGuiderParams
-from ltx_core.components.noisers import GaussianNoiser
-from ltx_core.components.protocols import DiffusionStepProtocol
-from ltx_core.components.schedulers import LTX2Scheduler
-from ltx_core.model.audio_vae import encode_audio as vae_encode_audio
-from ltx_core.model.audio_vae import decode_audio as vae_decode_audio
-from ltx_core.model.upsampler import upsample_video
-from ltx_core.model.video_vae import TilingConfig, get_video_chunks_number, decode_video as vae_decode_video
-from ltx_core.quantization import QuantizationPolicy
-from ltx_core.types import Audio, AudioLatentShape, VideoLatentShape, VideoPixelShape
-from ltx_pipelines.distilled import DistilledPipeline
-from ltx_pipelines.utils import (
-    res2s_audio_video_denoising_loop,
-    euler_denoising_loop,
-    cleanup_memory,
-    encode_prompts,
-    denoise_audio_video,
-)
-from ltx_pipelines.utils.args import ImageConditioningInput
-from ltx_pipelines.utils.constants import STAGE_2_DISTILLED_SIGMA_VALUES
-from ltx_pipelines.utils.helpers import (
-    combined_image_conditionings,
-    denoise_video_only,
-    simple_denoising_func,
-    multi_modal_guider_denoising_func,
-)
-from ltx_pipelines.utils.media_io import decode_audio_from_file, encode_video
-from ltx_core.loader.primitives import LoraPathStrengthAndSDOps
-from ltx_core.loader.sd_ops import LTXV_LORA_COMFY_RENAMING_MAP
-
-from ltx_core.model.transformer import attention as _attn_mod
-
-print(f"[ATTN] Before patch: memory_efficient_attention={_attn_mod.memory_efficient_attention}")
-try:
-    from xformers.ops import memory_efficient_attention as _mea
-    from xformers.ops.fmha import cutlass
-
-    def _cutlass_memory_efficient_attention(*args, **kwargs):
-        # Force CUTLASS and avoid FlashAttention paths that are crashing.
-        kwargs["op"] = (cutlass.FwOp, cutlass.BwOp)
-        return _mea(*args, **kwargs)
-
-    _attn_mod.memory_efficient_attention = _cutlass_memory_efficient_attention
-    print(f"[ATTN] After patch: memory_efficient_attention={_attn_mod.memory_efficient_attention}")
-except Exception as e:
-    print(f"[ATTN] xformers patch FAILED: {type(e).__name__}: {e}")
-
-logging.getLogger().setLevel(logging.INFO)
-
-MAX_SEED = np.iinfo(np.int32).max
-DEFAULT_PROMPT = (
-    "An astronaut hatches from a fragile egg on the surface of the Moon, "
-    "the shell cracking and peeling apart in gentle low-gravity motion. "
-    "Fine lunar dust lifts and drifts outward with each movement, floating "
-    "in slow arcs before settling back onto the ground."
-)
-
-DEFAULT_FRAME_RATE = 24.0
-
-# Resolution presets: (width, height)
-RESOLUTIONS = {
-    "low":  {"16:9": (768, 512), "9:16": (512, 768), "1:1": (768, 768),
-             "4:3": (768, 576), "3:4": (576, 768), "21:9": (768, 384)},
-    "high": {"16:9": (1536, 1024), "9:16": (1024, 1536), "1:1": (1024, 1024),
-             "4:3": (1536, 1152), "3:4": (1152, 1536), "21:9": (1536, 768)},
-}
-
-class LTX23DistilledA2VPipeline:
-    """Standalone pipeline with optional audio conditioning — no parent class."""
-
-    def __init__(
-        self,
-        distilled_checkpoint_path: str,
-        spatial_upsampler_path: str,
-        gemma_root: str,
-        loras: tuple,
-        quantization: QuantizationPolicy | None = None,
-    ):
-        from ltx_pipelines.utils import ModelLedger, denoise_audio_video
-        from ltx_pipelines.utils.types import PipelineComponents
-
-        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        self.dtype = torch.bfloat16
-
-        self.model_ledger = ModelLedger(
-            dtype=self.dtype,
-            device=self.device,
-            checkpoint_path=distilled_checkpoint_path,
-            gemma_root_path=gemma_root,
-            spatial_upsampler_path=spatial_upsampler_path,
-            loras=loras,
-            quantization=quantization,
-        )
-
-        self.pipeline_components = PipelineComponents(
-            dtype=self.dtype,
-            device=self.device,
-        )
-
-    def __call__(
-        self,
-        prompt: str,
-        seed: int,
-        height: int,
-        width: int,
-        num_frames: int,
-        frame_rate: float,
-        video_guider_params: MultiModalGuiderParams,
-        audio_guider_params: MultiModalGuiderParams,
-        images: list[ImageConditioningInput],
-        num_inference_steps: int = 8,
-        audio_path: str | None = None,
-        tiling_config: TilingConfig | None = None,
-        enhance_prompt: bool = False,
-    ):
-        print(prompt)
-
-        generator = torch.Generator(device=self.device).manual_seed(seed)
-        noiser = GaussianNoiser(generator=generator)
-        stepper = EulerDiffusionStep()
-        dtype = torch.bfloat16
-
-        ctx_p = encode_prompts(
-            [prompt],
-            self.model_ledger,
-            enhance_first_prompt=enhance_prompt,
-            enhance_prompt_image=images[0].path if len(images) > 0 else None,
- )[0]
-        v_context_p, a_context_p = ctx_p.video_encoding, ctx_p.audio_encoding
-
-        # ── Audio encoding (only for conditioning, not output generation) ──
-        encoded_audio_latent = None
-        decoded_audio = None
-        if audio_path is not None:
-            video_duration = num_frames / frame_rate
-            decoded_audio = decode_audio_from_file(audio_path, self.device, 0.0, video_duration)
-            if decoded_audio is None:
-                raise ValueError(f"Could not extract audio stream from {audio_path}")
-
-            encoded_audio_latent = vae_encode_audio(decoded_audio, self.model_ledger.audio_encoder())
-            audio_shape = AudioLatentShape.from_duration(batch=1, duration=video_duration, channels=8, mel_bins=16)
-            expected_frames = audio_shape.frames
-            actual_frames = encoded_audio_latent.shape[2]
-
-            if actual_frames > expected_frames:
-                encoded_audio_latent = encoded_audio_latent[:, :, :expected_frames, :]
-            elif actual_frames < expected_frames:
-                pad = torch.zeros(
-                    encoded_audio_latent.shape[0],
-                    encoded_audio_latent.shape[1],
-                    expected_frames - actual_frames,
-                    encoded_audio_latent.shape[3],
-                    device=encoded_audio_latent.device,
-                    dtype=encoded_audio_latent.dtype,
-                )
-                encoded_audio_latent = torch.cat([encoded_audio_latent, pad], dim=2)
-
-        video_encoder = self.model_ledger.video_encoder()
-        transformer = self.model_ledger.transformer()
-
-        # Stage 1: Generate sigmas using LTX2Scheduler with user-specified steps
-        empty_latent = torch.empty(VideoLatentShape.from_pixel_shape(
-            VideoPixelShape(batch=1, frames=num_frames, width=width, height=height, fps=frame_rate)
-        ).to_torch_shape())
-        stage_1_sigmas = (
-            LTX2Scheduler()
-            .execute(latent=empty_latent, steps=num_inference_steps)
-            .to(dtype=torch.float32, device=self.device)
-        )
-
-        def stage1_denoising_loop(sigmas: torch.Tensor, video_state, audio_state, stepper: DiffusionStepProtocol):
-            return euler_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,
-                    ),
-                    audio_guider=MultiModalGuider(
-                        params=audio_guider_params,
-                    ),
-                    v_context=v_context_p,
-                    a_context=a_context_p,
-                    transformer=transformer,
-                ),
-            )
-
-        # ── Stage 1: Half resolution ──
-        stage_1_output_shape = VideoPixelShape(
-            batch=1,
-            frames=num_frames,
-            width=width,
-            height=height,
-            fps=frame_rate,
-        )
-        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=self.device,
-        )
-
-        # Use denoise_audio_video so audio is ALWAYS generated
-        from ltx_pipelines.utils import denoise_audio_video
-        video_state, audio_state = denoise_audio_video(
-            output_shape=stage_1_output_shape,
-            conditionings=stage_1_conditionings,
-            noiser=noiser,
-            sigmas=stage_1_sigmas,
-            stepper=stepper,
-            denoising_loop_fn=stage1_denoising_loop,
-            components=self.pipeline_components,
-            dtype=dtype,
-            device=self.device,
-            initial_audio_latent=encoded_audio_latent,
-        )
-
-        torch.cuda.synchronize()
-        cleanup_memory()
-
-        # ── Decode both video and audio ──
-        decoded_video = vae_decode_video(
-            video_state.latent,
-            self.model_ledger.video_decoder(),
-            tiling_config,
-            generator,
-        )
-        decoded_audio_output = vae_decode_audio(
-            audio_state.latent,
-            self.model_ledger.audio_decoder(),
-            self.model_ledger.vocoder(),
-        )
-
-        return decoded_video, decoded_audio_output
-
-# Model repos
-LTX_MODEL_REPO = "SulphurAI/Sulphur-2-base"
-GEMMA_REPO ="Lightricks/gemma-3-12b-it-qat-q4_0-unquantized"
-
-# Download model checkpoints
-print("=" * 80)
-print("Downloading LTX-2.3 distilled model + Gemma...")
-print("=" * 80)
-
-# LoRA cache directory and currently-applied key
-LORA_CACHE_DIR = Path("lora_cache")
-LORA_CACHE_DIR.mkdir(exist_ok=True)
-current_lora_key: str | None = None
-
-PENDING_LORA_KEY: str | None = None
-PENDING_LORA_STATE: dict[str, torch.Tensor] | None = None
-PENDING_LORA_STATUS: str = "No LoRA state prepared yet."
-
-weights_dir = Path("weights")
-weights_dir.mkdir(exist_ok=True)
-checkpoint_path = hf_hub_download(
-    repo_id=LTX_MODEL_REPO,
-    filename="sulphur_distil_bf16.safetensors",
-    local_dir=str(weights_dir),
-    local_dir_use_symlinks=False,
-)
-spatial_upsampler_path = hf_hub_download(repo_id="Lightricks/LTX-2.3", filename="ltx-2.3-spatial-upscaler-x2-1.1.safetensors")
-gemma_root = snapshot_download(repo_id=GEMMA_REPO)
-
-
-LORA_REPO = "dagloop5/LoRA"
-
-print("=" * 80)
-print("Downloading LoRA adapters from dagloop5/LoRA...")
-print("=" * 80)
-pose_lora_path = hf_hub_download(repo_id=LORA_REPO, filename="LTX2_3_NSFW_furry_concat_v2.safetensors")
-twod_lora_path = hf_hub_download(repo_id=LORA_REPO, filename="LTX2.3_2d_NSFW_motion_enhancer.safetensors") # 2d style animation
-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="Sulphur_LTX 2.3_better _NSFW_motion.safetensors")
-dreamlay_lora_path = hf_hub_download(repo_id="lynaNSFW/DR34ML4Y_AIO_NSFW_LTX23", filename="DR34ML4Y_LTXXX_V2.safetensors") # m15510n4ry, bl0wj0b, d0ubl3_bj, d0gg1e, c0wg1rl
-mself_lora_path = hf_hub_download(repo_id=LORA_REPO, filename="Furry Hyper Masturbation - LTX-2 I2V v1.safetensors") # Hyperfap
-dramatic_lora_path = hf_hub_download(repo_id=LORA_REPO, filename="LTX-2.3 - Orgasm.safetensors") # "[He | She] is having am orgasm." (am or an?)
-fluid_lora_path = hf_hub_download(repo_id=LORA_REPO, filename="Cr3ampi3_animation_sulphur-2_i2v_v1.0.safetensors") # cr3ampi3 animation
-liquid_lora_path = hf_hub_download(repo_id=LORA_REPO, filename="liquid_wet_dr1pp_ltx2_v1.0_scaled.safetensors") # wet dr1pp
-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="FurryenhancerLTX2.3V4.094fused.safetensors")
-transition_lora_path = hf_hub_download(repo_id=LORA_REPO, filename="LTX-2_takerpov_lora_v1.2.safetensors") # takerpov1, taker pov
-physics_lora_path = hf_hub_download(repo_id=LORA_REPO, filename="LTX2.3_Physics_V2_000002000.safetensors")
-reasoning_lora_path = hf_hub_download(repo_id="LiconStudio/Ltx2.3-VBVR-lora-I2V", filename="Ltx2.3-Licon-VBVR-I2V-390K-R32.safetensors")
-twostep_lora_path = hf_hub_download(repo_id=LORA_REPO, filename="LTX2.3_Multi_step_video_reasoning_V0.1.safetensors")
-
-print(f"Pose LoRA: {pose_lora_path}")
-print(f"twod LoRA: {twod_lora_path}")
-print(f"General LoRA: {general_lora_path}")
-print(f"Motion LoRA: {motion_lora_path}")
-print(f"Dreamlay LoRA: {dreamlay_lora_path}")
-print(f"Mself LoRA: {mself_lora_path}")
-print(f"Dramatic LoRA: {dramatic_lora_path}")
-print(f"Fluid LoRA: {fluid_lora_path}")
-print(f"Liquid LoRA: {liquid_lora_path}")
-print(f"Demopose LoRA: {demopose_lora_path}")
-print(f"Voice LoRA: {voice_lora_path}")
-print(f"Realism LoRA: {realism_lora_path}")
-print(f"Transition LoRA: {transition_lora_path}")
-print(f"Physics LoRA: {physics_lora_path}")
-print(f"Reasoning LoRA: {reasoning_lora_path}")
-print(f"Twostep LoRA: {twostep_lora_path}")
-
-print(f"Checkpoint: {checkpoint_path}")
-print(f"Spatial upsampler: {spatial_upsampler_path}")
-print(f"[Gemma] Root ready: {gemma_root}")
-
-pipeline = LTX23DistilledA2VPipeline(
-    distilled_checkpoint_path=checkpoint_path,
-    spatial_upsampler_path=spatial_upsampler_path,
-    gemma_root=gemma_root,
-    loras=[],
-    quantization=QuantizationPolicy.fp8_cast(),
-)
-
-def _make_lora_key(pose_strength: float, twod_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, physics_strength: float, reasoning_strength: float, twostep_strength: float) -> tuple[str, str]:
-    rp = round(float(pose_strength), 2)
-    ra = round(float(twod_strength), 2)
-    rg = round(float(general_strength), 2)
-    rm = round(float(motion_strength), 2)
-    rd = round(float(dreamlay_strength), 2)
-    rs = round(float(mself_strength), 2)
-    rr = round(float(dramatic_strength), 2)
-    rf = round(float(fluid_strength), 2)
-    rl = round(float(liquid_strength), 2)
-    ro = round(float(demopose_strength), 2)
-    rv = round(float(voice_strength), 2)
-    re = round(float(realism_strength), 2)
-    rt = round(float(transition_strength), 2)
-    ry = round(float(physics_strength), 2)
-    ri = round(float(reasoning_strength), 2)
-    rw = round(float(twostep_strength), 2)
-    key_str = f"{pose_lora_path}:{rp}|{twod_lora_path}:{ra}|{general_lora_path}:{rg}|{motion_lora_path}:{rm}|{dreamlay_lora_path}:{rd}|{mself_lora_path}:{rs}|{dramatic_lora_path}:{rr}|{fluid_lora_path}:{rf}|{liquid_lora_path}:{rl}|{demopose_lora_path}:{ro}|{voice_lora_path}:{rv}|{realism_lora_path}:{re}|{transition_lora_path}:{rt}|{physics_lora_path}:{ry}|{reasoning_lora_path}:{ri}|{twostep_lora_path}:{rw}"
-    key = hashlib.sha256(key_str.encode("utf-8")).hexdigest()
-    return key, key_str
-
-
-def prepare_lora_cache(
-    pose_strength: float,
-    twod_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,
-    physics_strength: float,
-    reasoning_strength: float,
-    twostep_strength: float,
-    progress=gr.Progress(track_tqdm=True),
-):
-    """
-    CPU-only step:
-    - checks cache
-    - loads cached fused transformer state_dict, or
-    - builds fused transformer on CPU and saves it
-    The resulting state_dict is stored in memory and can be applied later.
-    """
-    global PENDING_LORA_KEY, PENDING_LORA_STATE, PENDING_LORA_STATUS
-
-    ledger = pipeline.model_ledger
-    key, _ = _make_lora_key(pose_strength, twod_strength, general_strength, motion_strength, dreamlay_strength, mself_strength, dramatic_strength, fluid_strength, liquid_strength, demopose_strength, voice_strength, realism_strength, transition_strength, physics_strength, reasoning_strength, twostep_strength)
-    cache_path = LORA_CACHE_DIR / f"{key}.safetensors"
-
-    progress(0.05, desc="Preparing LoRA state")
-    if cache_path.exists():
-        try:
-            progress(0.20, desc="Loading cached fused state")
-            state = load_file(str(cache_path))
-            PENDING_LORA_KEY = key
-            PENDING_LORA_STATE = state
-            PENDING_LORA_STATUS = f"Loaded cached LoRA state: {cache_path.name}"
-            return PENDING_LORA_STATUS
-        except Exception as e:
-            print(f"[LoRA] Cache load failed: {type(e).__name__}: {e}")
-
-    entries = [
-        (pose_lora_path, round(float(pose_strength), 2)),
-        (twod_lora_path, round(float(twod_strength), 2)),
-        (general_lora_path, round(float(general_strength), 2)),
-        (motion_lora_path, round(float(motion_strength), 2)),
-        (dreamlay_lora_path, round(float(dreamlay_strength), 2)),
-        (mself_lora_path, round(float(mself_strength), 2)),
-        (dramatic_lora_path, round(float(dramatic_strength), 2)),
-        (fluid_lora_path, round(float(fluid_strength), 2)),
-        (liquid_lora_path, round(float(liquid_strength), 2)),
-        (demopose_lora_path, round(float(demopose_strength), 2)),
-        (voice_lora_path, round(float(voice_strength), 2)),
-        (realism_lora_path, round(float(realism_strength), 2)),
-        (transition_lora_path, round(float(transition_strength), 2)),
-        (physics_lora_path, round(float(physics_strength), 2)),
-        (reasoning_lora_path, round(float(reasoning_strength), 2)),
-        (twostep_lora_path, round(float(twostep_strength), 2)),
-    ]
-    loras_for_builder = [
-        LoraPathStrengthAndSDOps(path, strength, LTXV_LORA_COMFY_RENAMING_MAP)
-        for path, strength in entries
-        if path is not None and float(strength) != 0.0
-    ]
-
-    if not loras_for_builder:
-        PENDING_LORA_KEY = None
-        PENDING_LORA_STATE = None
-        PENDING_LORA_STATUS = "No non-zero LoRA strengths selected; nothing to prepare."
-        return PENDING_LORA_STATUS
-
-    tmp_ledger = None
-    new_transformer_cpu = None
-    try:
-        progress(0.35, desc="Building fused CPU transformer")
-        tmp_ledger = pipeline.model_ledger.__class__(
-            dtype=ledger.dtype,
-            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_for_builder),
-            quantization=getattr(ledger, "quantization", None),
-        )
-        new_transformer_cpu = tmp_ledger.transformer()
-
-        progress(0.70, desc="Extracting fused state_dict")
-        state = {
-            k: v.detach().cpu().contiguous()
-            for k, v in new_transformer_cpu.state_dict().items()
-        }
-        save_file(state, str(cache_path))
-
-        PENDING_LORA_KEY = key
-        PENDING_LORA_STATE = state
-        PENDING_LORA_STATUS = f"Built and cached LoRA state: {cache_path.name}"
-        return PENDING_LORA_STATUS
-
-    except Exception as e:
-        import traceback
-        print(f"[LoRA] Prepare failed: {type(e).__name__}: {e}")
-        print(traceback.format_exc())
-        PENDING_LORA_KEY = None
-        PENDING_LORA_STATE = None
-        PENDING_LORA_STATUS = f"LoRA prepare failed: {type(e).__name__}: {e}"
-        return PENDING_LORA_STATUS
-
-    finally:
-        try:
-            del new_transformer_cpu
-        except Exception:
-            pass
-        try:
-            del tmp_ledger
-        except Exception:
-            pass
-        gc.collect()
-
-
-def apply_prepared_lora_state_to_pipeline():
-    """
-    Fast step: copy the already prepared CPU state into the live transformer.
-    This is the only part that should remain near generation time.
-    """
-    global current_lora_key, PENDING_LORA_KEY, PENDING_LORA_STATE
-
-    if PENDING_LORA_STATE is None or PENDING_LORA_KEY is None:
-        print("[LoRA] No prepared LoRA state available; skipping.")
-        return False
-
-    if current_lora_key == PENDING_LORA_KEY:
-        print("[LoRA] Prepared LoRA state already active; skipping.")
-        return True
-
-    existing_transformer = _transformer
-    with torch.no_grad():
-        missing, unexpected = existing_transformer.load_state_dict(PENDING_LORA_STATE, strict=False)
-        if missing or unexpected:
-            print(f"[LoRA] load_state_dict mismatch: missing={len(missing)}, unexpected={len(unexpected)}")
-
-    current_lora_key = PENDING_LORA_KEY
-    print("[LoRA] Prepared LoRA state applied to the pipeline.")
-    return True
-
-# Preload all models for ZeroGPU tensor packing.
-print("Preloading all models (including Gemma and audio components)...")
-ledger = pipeline.model_ledger
-
-# Save the original factory methods so we can rebuild individual components later.
-# These are bound callables on ledger that will call the builder when invoked.
-_orig_transformer_factory = ledger.transformer
-_orig_video_encoder_factory = ledger.video_encoder
-_orig_video_decoder_factory = ledger.video_decoder
-_orig_audio_encoder_factory = ledger.audio_encoder
-_orig_audio_decoder_factory = ledger.audio_decoder
-_orig_vocoder_factory = ledger.vocoder
-_orig_spatial_upsampler_factory = ledger.spatial_upsampler
-_orig_text_encoder_factory = ledger.text_encoder
-_orig_gemma_embeddings_factory = ledger.gemma_embeddings_processor
-
-# Call the original factories once to create the cached instances we will serve by default.
-_transformer = _orig_transformer_factory()
-_video_encoder = _orig_video_encoder_factory()
-_video_decoder = _orig_video_decoder_factory()
-_audio_encoder = _orig_audio_encoder_factory()
-_audio_decoder = _orig_audio_decoder_factory()
-_vocoder = _orig_vocoder_factory()
-_spatial_upsampler = _orig_spatial_upsampler_factory()
-_text_encoder = _orig_text_encoder_factory()
-_embeddings_processor = _orig_gemma_embeddings_factory()
-
-# Replace ledger methods with lightweight lambdas that return the cached instances.
-# We keep the original factories above so we can call them later to rebuild components.
-ledger.transformer = lambda: _transformer
-ledger.video_encoder = lambda: _video_encoder
-ledger.video_decoder = lambda: _video_decoder
-ledger.audio_encoder = lambda: _audio_encoder
-ledger.audio_decoder = lambda: _audio_decoder
-ledger.vocoder = lambda: _vocoder
-ledger.spatial_upsampler = lambda: _spatial_upsampler
-ledger.text_encoder = lambda: _text_encoder
-ledger.gemma_embeddings_processor = lambda: _embeddings_processor
-
-print("All models preloaded (including Gemma text encoder and audio encoder)!")
-# ---- REPLACE PRELOAD BLOCK END ----
-
-print("=" * 80)
-print("Pipeline ready!")
-print("=" * 80)
-
-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 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,
-        "4:3": 4 / 3, "3:4": 3 / 4, "21:9": 21 / 9,
-    }
-    return min(candidates, key=lambda k: abs(ratio - candidates[k]))
-
-
-def on_image_upload(first_image, last_image, high_res):
-    ref_image = first_image if first_image is not None else last_image
-    aspect = detect_aspect_ratio(ref_image)
-    tier = "high" if high_res else "low"
-    w, h = RESOLUTIONS[tier][aspect]
-    return gr.update(value=w), gr.update(value=h)
-
-
-def on_highres_toggle(first_image, last_image, high_res):
-    ref_image = first_image if first_image is not None else last_image
-    aspect = detect_aspect_ratio(ref_image)
-    tier = "high" if high_res else "low"
-    w, h = RESOLUTIONS[tier][aspect]
-    return gr.update(value=w), gr.update(value=h)
-
-def get_gpu_duration(
-    first_image,
-    last_image,
-    input_audio,
-    prompt: str,
-    duration: float,
-    gpu_duration: float,
-    enhance_prompt: bool = True,
-    seed: int = 42,
-    randomize_seed: bool = True,
-    height: int = 1024,
-    width: int = 1536,
-    video_cfg_scale: float = 1.0,
-    video_stg_scale: float = 0.0,
-    video_rescale_scale: float = 0.45,
-    video_a2v_scale: float = 3.0,
-    audio_cfg_scale: float = 1.0,
-    audio_stg_scale: float = 0.0,
-    audio_rescale_scale: float = 1.0,
-    audio_v2a_scale: float = 3.0,
-    pose_strength: float = 0.0,
-    twod_strength: float = 0.0,
-    general_strength: float = 0.0,
-    motion_strength: float = 0.0,
-    dreamlay_strength: float = 0.0,
-    mself_strength: float = 0.0,
-    dramatic_strength: float = 0.0,
-    fluid_strength: float = 0.0,
-    liquid_strength: float = 0.0,
-    demopose_strength: float = 0.0,
-    voice_strength: float = 0.0,
-    realism_strength: float = 0.0,
-    transition_strength: float = 0.0,
-    physics_strength: float = 0.0,
-    reasoning_strength: float = 0.0,
-    twostep_strength: float = 0.0,
-    num_inference_steps: int = 8,
-    progress=None,
-):
-    return int(gpu_duration)
-
-@spaces.GPU(duration=get_gpu_duration)
-@torch.inference_mode()
-def generate_video(
-    first_image,
-    last_image,
-    input_audio,
-    prompt: str,
-    duration: float,
-    gpu_duration: float,
-    enhance_prompt: bool = True,
-    seed: int = 42,
-    randomize_seed: bool = True,
-    height: int = 1024,
-    width: int = 1536,
-    video_cfg_scale: float = 1.0,
-    video_stg_scale: float = 0.0,
-    video_rescale_scale: float = 0.45,
-    video_a2v_scale: float = 3.0,
-    audio_cfg_scale: float = 1.0,
-    audio_stg_scale: float = 0.0,
-    audio_rescale_scale: float = 1.0,
-    audio_v2a_scale: float = 3.0,
-    pose_strength: float = 0.0,
-    twod_strength: float = 0.0,
-    general_strength: float = 0.0,
-    motion_strength: float = 0.0,
-    dreamlay_strength: float = 0.0,
-    mself_strength: float = 0.0,
-    dramatic_strength: float = 0.0,
-    fluid_strength: float = 0.0,
-    liquid_strength: float = 0.0,
-    demopose_strength: float = 0.0,
-    voice_strength: float = 0.0,
-    realism_strength: float = 0.0,
-    transition_strength: float = 0.0,
-    physics_strength: float = 0.0,
-    reasoning_strength: float = 0.0,
-    twostep_strength: float = 0.0,
-    num_inference_steps: int = 8,
-    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)
-
-        frame_rate = DEFAULT_FRAME_RATE
-        num_frames = int(duration * frame_rate) + 1
-        num_frames = ((num_frames - 1 + 7) // 8) * 8 + 1
-
-        print(f"Generating: {height}x{width}, {num_frames} frames ({duration}s), seed={current_seed}")
-
-        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:
-                temp_first_path = Path(first_image)
-            images.append(ImageConditioningInput(path=str(temp_first_path), frame_idx=0, strength=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:
-                temp_last_path = Path(last_image)
-            images.append(ImageConditioningInput(path=str(temp_last_path), frame_idx=num_frames - 1, strength=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")
-
-        apply_prepared_lora_state_to_pipeline()
-
-        video, audio = pipeline(
-            prompt=prompt,
-            seed=current_seed,
-            height=int(height),
-            width=int(width),
-            num_frames=num_frames,
-            frame_rate=frame_rate,
-            video_guider_params=video_guider_params,
-            audio_guider_params=audio_guider_params,
-            images=images,
-            num_inference_steps=num_inference_steps,
-            audio_path=input_audio,
-            tiling_config=tiling_config,
-            enhance_prompt=enhance_prompt,
-        )
-
-        log_memory("after pipeline call")
-
-        output_path = tempfile.mktemp(suffix=".mp4")
-        encode_video(
-            video=video,
-            fps=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
-# =============================================================================
-
-css = """
-.fillable {max-width: 1200px !important}
-.progress-text {color: black}
-"""
-
-with gr.Blocks(title="LTX-2.3 Distilled with LoRAs, Negative Prompting, and Advanced Settings") as demo:
-    gr.Markdown("# LTX-2.3 Two-Stage HQ Video Generation")
-    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():
-        # LEFT SIDE: Input Controls
-        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="Make this image come alive with cinematic motion, smooth animation",
-                lines=3,
-                placeholder="Describe the motion and animation you want...",
-            )
-            
-            duration = gr.Slider(
-                label="Duration (seconds)",
-                minimum=1.0, maximum=30.0, value=10.0, step=0.1,
-            )
-            
-            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)
-            
-            with gr.Row():
-                high_res = gr.Checkbox(label="High Resolution", value=True)
-                enhance_prompt = gr.Checkbox(label="Enhance Prompt", value=False)
-            
-            with gr.Row():
-                width = gr.Number(label="Width", value=1536, precision=0)
-                height = gr.Number(label="Height", value=1024, precision=0)
-
-            with gr.Row():
-                num_inference_steps = gr.Slider(
-                    label="Inference Steps",
-                    minimum=2, maximum=16, value=8, step=1,
-                     info="Higher = more quality but slower"
-                )
-            
-            generate_btn = gr.Button("Generate Video", variant="primary", size="lg")
-
-            with gr.Accordion("Advanced Settings", open=False):
-                gr.Markdown("### Video Guidance Parameters")
-                
-                with gr.Row():
-                    video_cfg_scale = gr.Slider(
-                        label="Video CFG Scale", minimum=1.0, maximum=10.0, value=1.0, 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=1.0, 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
-                    )
-            with gr.Row():
-                input_audio = gr.Audio(label="Audio Input (Optional)", type="filepath")
-
-        # RIGHT SIDE: Output and LoRA
-        with gr.Column():
-            output_video = gr.Video(label="Generated Video", autoplay=False)
-            
-            gpu_duration = gr.Slider(
-                label="ZeroGPU duration (seconds)",
-                minimum=30.0, maximum=240.0, value=90.0, step=1.0,
-                info="Increase for longer videos, higher resolution, or LoRA usage"
-            )
-            
-            gr.Markdown("### LoRA Adapter Strengths")
-            gr.Markdown("Set to 0 to disable, then click 'Prepare LoRA Cache'")
-            
-            with gr.Row():
-                pose_strength = gr.Slider(label="Anthro Enhancer", minimum=0.0, maximum=2.0, value=0.0, step=0.01)
-                twod_strength = gr.Slider(label="2D Enhancer", minimum=0.0, maximum=2.0, value=0.0, step=0.01)
-            
-            with gr.Row():
-                general_strength = gr.Slider(label="Reasoning Enhancer", minimum=0.0, maximum=2.0, value=0.0, step=0.01)
-                motion_strength = gr.Slider(label="Anthro Posing", minimum=0.0, maximum=2.0, value=0.0, step=0.01)
-            
-            with gr.Row():
-                dreamlay_strength = gr.Slider(label="Dreamlay", minimum=0.0, maximum=2.0, value=0.0, step=0.01)
-                mself_strength = gr.Slider(label="Mself", minimum=0.0, maximum=2.0, value=0.0, step=0.01)
-            
-            with gr.Row():
-                dramatic_strength = gr.Slider(label="Dramatic", minimum=0.0, maximum=2.0, value=0.0, step=0.01)
-                fluid_strength = gr.Slider(label="Fluid Helper", minimum=0.0, maximum=2.0, value=0.0, step=0.01)
-            
-            with gr.Row():
-                liquid_strength = gr.Slider(label="Liquid Helper", minimum=0.0, maximum=2.0, value=0.0, step=0.01)
-                demopose_strength = gr.Slider(label="Audio Helper", minimum=0.0, maximum=2.0, value=0.0, step=0.01)
-            
-            with gr.Row():
-                voice_strength = gr.Slider(label="Voice Helper", minimum=0.0, maximum=2.0, value=0.0, step=0.01)
-                realism_strength = gr.Slider(label="Anthro Realism", minimum=0.0, maximum=2.0, value=0.0, step=0.01)
-            
-            with gr.Row():
-                transition_strength = gr.Slider(label="POV", minimum=0.0, maximum=2.0, value=0.0, step=0.01)
-                physics_strength = gr.Slider(label="Physics", minimum=0.0, maximum=2.0, value=0.0, step=0.01)
-            with gr.Row():
-                reasoning_strength = gr.Slider(label="Official Reasoning", minimum=0.0, maximum=2.0, value=0.0, step=0.01)
-                twostep_strength = gr.Slider(label="Two Step Reasoning", 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,
-            )
-
-    # Event handlers
-    first_image.change(fn=on_image_upload, inputs=[first_image, last_image, high_res], outputs=[width, height])
-    last_image.change(fn=on_image_upload, inputs=[first_image, last_image, high_res], outputs=[width, height])
-    high_res.change(fn=on_highres_toggle, inputs=[first_image, last_image, high_res], outputs=[width, height])
-
-    prepare_lora_btn.click(
-        fn=prepare_lora_cache,
-        inputs=[pose_strength, twod_strength, general_strength, motion_strength, dreamlay_strength,
-                mself_strength, dramatic_strength, fluid_strength, liquid_strength,
-                demopose_strength, voice_strength, realism_strength, transition_strength, physics_strength, reasoning_strength, twostep_strength],
-        outputs=[lora_status],
-    )
-
-    generate_btn.click(
-        fn=generate_video,
-        inputs=[
-            first_image, last_image, input_audio, prompt, duration, gpu_duration,
-            enhance_prompt, seed, randomize_seed, height, width,
-            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, twod_strength, general_strength, motion_strength,
-            dreamlay_strength, mself_strength, dramatic_strength, fluid_strength,
-            liquid_strength, demopose_strength, voice_strength, realism_strength,
-            transition_strength, physics_strength, reasoning_strength, twostep_strength, num_inference_steps,
-        ],
-        outputs=[output_video, seed],
-    )
-
-
-if __name__ == "__main__":
-    demo.queue().launch(theme=gr.themes.Citrus(), css=css, mcp_server=False)