Create app.py

app.py

@@ -0,0 +1,891 @@
+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"
+
+# Install xformers for memory-efficient attention
+subprocess.run([sys.executable, "-m", "pip", "install", "xformers==0.0.32.post2", "--no-build-isolation"], check=False)
+
+# 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 ltx_core.components.diffusion_steps import EulerDiffusionStep
+from ltx_core.components.noisers import GaussianNoiser
+from ltx_core.model.audio_vae import encode_audio as vae_encode_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, VideoPixelShape
+from ltx_pipelines.distilled import DistilledPipeline
+from ltx_pipelines.utils import euler_denoising_loop
+from ltx_pipelines.utils.args import ImageConditioningInput
+from ltx_pipelines.utils.constants import DISTILLED_SIGMA_VALUES, STAGE_2_DISTILLED_SIGMA_VALUES
+from ltx_pipelines.utils.helpers import (
+    cleanup_memory,
+    combined_image_conditionings,
+    denoise_video_only,
+    encode_prompts,
+    simple_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
+
+# Force-patch xformers attention into the LTX attention module.
+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
+    _attn_mod.memory_efficient_attention = _mea
+    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 = {
+    "high": {"16:9": (1536, 1024), "9:16": (1024, 1536), "1:1": (1024, 1024), "9:7": (1408, 1088), "7:9": (1088, 1408), "19:13": (1472, 1008), "13:19": (1008, 1472)},
+    "low": {"16:9": (768, 512), "9:16": (512, 768), "1:1": (768, 768), "9:7": (704, 544), "7:9": (544, 704), "19:13": (736, 504), "13:19": (504, 736)},
+}
+
+
+class LTX23DistilledA2VPipeline(DistilledPipeline):
+    """DistilledPipeline with optional audio conditioning."""
+
+    def __call__(
+        self,
+        prompt: str,
+        seed: int,
+        height: int,
+        width: int,
+        num_frames: int,
+        frame_rate: float,
+        images: list[ImageConditioningInput],
+        audio_path: str | None = None,
+        tiling_config: TilingConfig | None = None,
+        enhance_prompt: bool = False,
+    ):
+        # Standard path when no audio input is provided.
+        print(prompt)
+        if audio_path is None:
+            return super().__call__(
+                prompt=prompt,
+                seed=seed,
+                height=height,
+                width=width,
+                num_frames=num_frames,
+                frame_rate=frame_rate,
+                images=images,
+                tiling_config=tiling_config,
+                enhance_prompt=enhance_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,
+        )
+        video_context, audio_context = ctx_p.video_encoding, ctx_p.audio_encoding
+
+        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_sigmas = torch.tensor(DISTILLED_SIGMA_VALUES, device=self.device)
+
+        def denoising_loop(sigmas, video_state, audio_state, stepper):
+            return euler_denoising_loop(
+                sigmas=sigmas,
+                video_state=video_state,
+                audio_state=audio_state,
+                stepper=stepper,
+                denoise_fn=simple_denoising_func(
+                    video_context=video_context,
+                    audio_context=audio_context,
+                    transformer=transformer,
+                ),
+            )
+
+        stage_1_output_shape = VideoPixelShape(
+            batch=1,
+            frames=num_frames,
+            width=width // 2,
+            height=height // 2,
+            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,
+        )
+        video_state = denoise_video_only(
+            output_shape=stage_1_output_shape,
+            conditionings=stage_1_conditionings,
+            noiser=noiser,
+            sigmas=stage_1_sigmas,
+            stepper=stepper,
+            denoising_loop_fn=denoising_loop,
+            components=self.pipeline_components,
+            dtype=dtype,
+            device=self.device,
+            initial_audio_latent=encoded_audio_latent,
+        )
+
+        torch.cuda.synchronize()
+        cleanup_memory()
+
+        upscaled_video_latent = upsample_video(
+            latent=video_state.latent[:1],
+            video_encoder=video_encoder,
+            upsampler=self.model_ledger.spatial_upsampler(),
+        )
+        stage_2_sigmas = torch.tensor(STAGE_2_DISTILLED_SIGMA_VALUES, device=self.device)
+        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=self.device,
+        )
+        video_state = denoise_video_only(
+            output_shape=stage_2_output_shape,
+            conditionings=stage_2_conditionings,
+            noiser=noiser,
+            sigmas=stage_2_sigmas,
+            stepper=stepper,
+            denoising_loop_fn=denoising_loop,
+            components=self.pipeline_components,
+            dtype=dtype,
+            device=self.device,
+            noise_scale=stage_2_sigmas[0],
+            initial_video_latent=upscaled_video_latent,
+            initial_audio_latent=encoded_audio_latent,
+        )
+
+        torch.cuda.synchronize()
+        del transformer
+        del video_encoder
+        cleanup_memory()
+
+        decoded_video = vae_decode_video(
+            video_state.latent,
+            self.model_ledger.video_decoder(),
+            tiling_config,
+            generator,
+        )
+        original_audio = Audio(
+            waveform=decoded_audio.waveform.squeeze(0),
+            sampling_rate=decoded_audio.sampling_rate,
+        )
+        return decoded_video, original_audio
+
+
+# Model repos
+LTX_MODEL_REPO = "Lightricks/LTX-2.3"
+GEMMA_REPO ="Lightricks/gemma-3-12b-it-qat-q4_0-unquantized"
+EROS_REPO = "dagloop5/LoRA"
+EROS_FILE = "ltx2310eros_beta.safetensors"
+
+# 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."
+
+# --- 1. 10Eros checkpoint: FP8→BF16 conversion + DEV metadata injection ---
+# Official DEV checkpoint is BF16. fp8_cast() expects BF16 input.
+# 10Eros is FP8 (CivitAI format) → convert to BF16 to match official dtype distribution.
+print("[1/4] Preparing 10Eros checkpoint...")
+eros_fp8_path = hf_hub_download(repo_id=EROS_REPO, filename=EROS_FILE)
+print(f"  Downloaded: {eros_fp8_path}")
+
+EROS_FIXED = "/tmp/eros_bf16_with_meta.safetensors"
+if os.path.exists(EROS_FIXED):
+    print("  Using cached BF16 checkpoint")
+else:
+    # Fetch DEV checkpoint metadata from header only (first 2MB, not full 46GB)
+    print("  Fetching DEV checkpoint metadata (header only)...")
+    dev_url = f"https://huggingface.co/{LTX_OFFICIAL_REPO}/resolve/main/ltx-2.3-22b-dev.safetensors"
+    hdr_resp = requests.get(dev_url, headers={"Range": "bytes=0-2000000"}, timeout=30)
+    hdr_size = int.from_bytes(hdr_resp.content[:8], "little")
+    hdr_json = json.loads(hdr_resp.content[8:8 + min(hdr_size, len(hdr_resp.content) - 8)])
+    dev_metadata = hdr_json.get("__metadata__", {})
+    print(f"  DEV metadata keys: {list(dev_metadata.keys())}")
+
+    # Convert FP8→BF16 + inject metadata
+    print("  Converting FP8→BF16 (lossless upcast)...")
+    _fp8_types = {torch.float8_e4m3fn, torch.float8_e5m2}
+    tensors = {}
+    _converted = 0
+    with safe_open(eros_fp8_path, framework="pt") as f:
+        for key in f.keys():
+            tensor = f.get_tensor(key)
+            if tensor.dtype in _fp8_types:
+                tensors[key] = tensor.to(torch.bfloat16)
+                _converted += 1
+            else:
+                tensors[key] = tensor
+    print(f"  Converted {_converted} FP8→BF16, kept {len(tensors)-_converted} as-is")
+    save_file(tensors, EROS_FIXED, metadata=dev_metadata)
+    del tensors
+    gc.collect()
+    print("  Saved with DEV metadata")
+
+checkpoint_path = EROS_FIXED
+
+spatial_upsampler_path = hf_hub_download(repo_id=LTX_MODEL_REPO, filename="ltx-2.3-spatial-upscaler-x2-1.0.safetensors")
+gemma_root = snapshot_download(repo_id=GEMMA_REPO)
+
+# ---- Insert block (LoRA downloads) between lines 268 and 269 ----
+# LoRA repo + download the requested LoRA adapters
+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")
+general_lora_path = hf_hub_download(repo_id=LORA_REPO, filename="LTX2.3_VBVR_Reasoning_I2V_V2.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") # 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_i2v_ltx2_v1.0.safetensors") # cr3ampi3 animation., missionary animation, doggystyle bouncy animation, double penetration animation
+liquid_lora_path = hf_hub_download(repo_id="valiantcat/LTX-2.3-Transition-LORA", filename="ltx2.3-transition.safetensors") # wet dr1pp
+demopose_lora_path = hf_hub_download(repo_id=LORA_REPO, filename="hentai_voice_ltx23.safetensors")
+
+print(f"Pose LoRA: {pose_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"Checkpoint: {checkpoint_path}")
+print(f"Spatial upsampler: {spatial_upsampler_path}")
+print(f"Gemma root: {gemma_root}")
+
+# Initialize pipeline WITH text encoder and optional audio support
+# ---- Replace block (pipeline init) lines 275-281 ----
+pipeline = LTX23DistilledA2VPipeline(
+    distilled_checkpoint_path=checkpoint_path,
+    spatial_upsampler_path=spatial_upsampler_path,
+    gemma_root=gemma_root,
+    loras=[],
+    quantization=QuantizationPolicy.fp8_cast(),  # keep FP8 quantization unchanged
+)
+# ----------------------------------------------------------------
+
+def _make_lora_key(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) -> tuple[str, str]:
+    rp = round(float(pose_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)
+    key_str = f"{pose_lora_path}:{rp}|{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}"
+    key = hashlib.sha256(key_str.encode("utf-8")).hexdigest()
+    return key, key_str
+
+
+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,
+    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, general_strength, motion_strength, dreamlay_strength, mself_strength, dramatic_strength, fluid_strength, liquid_strength, demopose_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)),
+        (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)),
+    ]
+    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
+
+# ---- REPLACE PRELOAD BLOCK START ----
+# 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}
+    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,
+    pose_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,
+    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,
+    pose_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,
+    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)
+
+        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,
+            images=images,
+            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
+
+
+with gr.Blocks(title="LTX-2.3 Distilled") as demo:
+    gr.Markdown("# LTX-2.3 F2LF with Fast Audio-Video Generation with Frame Conditioning")
+    
+
+    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")
+            input_audio = gr.Audio(label="Audio Input (Optional)", type="filepath")
+            prompt = gr.Textbox(
+                label="Prompt",
+                info="for best results - make it as elaborate as possible",
+                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)
+                
+
+            generate_btn = gr.Button("Generate Video", variant="primary", size="lg")
+
+            with gr.Accordion("Advanced Settings", open=False):
+                seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, value=10, step=1)
+                randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
+                with gr.Row():
+                    width = gr.Number(label="Width", value=1536, precision=0)
+                    height = gr.Number(label="Height", value=1024, precision=0)
+                with gr.Row():
+                    enhance_prompt = gr.Checkbox(label="Enhance Prompt", value=False)
+                    high_res = gr.Checkbox(label="High Resolution", value=True)
+                with gr.Column():
+                    gr.Markdown("### LoRA adapter strengths (set to 0 to disable; slow and WIP)")
+                    pose_strength = gr.Slider(
+                        label="Anthro Enhancer strength",
+                        minimum=0.0, maximum=2.0, value=0.0, step=0.01
+                    )
+                    general_strength = gr.Slider(
+                        label="Reasoning Enhancer strength",
+                        minimum=0.0, maximum=2.0, value=0.0, step=0.01
+                    )
+                    motion_strength = gr.Slider(
+                        label="Anthro Posing Helper strength",
+                        minimum=0.0, maximum=2.0, value=0.0, step=0.01
+                    )
+                    dreamlay_strength = gr.Slider(
+                        label="Dreamlay strength",
+                        minimum=0.0, maximum=2.0, value=0.0, step=0.01
+                    )
+                    mself_strength = gr.Slider(
+                        label="Mself strength",
+                        minimum=0.0, maximum=2.0, value=0.0, step=0.01
+                    )
+                    dramatic_strength = gr.Slider(
+                        label="Dramatic strength",
+                        minimum=0.0, maximum=2.0, value=0.0, step=0.01
+                    )
+                    fluid_strength = gr.Slider(
+                        label="Fluid Helper strength",
+                        minimum=0.0, maximum=2.0, value=0.0, step=0.01
+                    )
+                    liquid_strength = gr.Slider(
+                        label="Transition Helper strength",
+                        minimum=0.0, maximum=2.0, value=0.0, step=0.01
+                    )
+                    demopose_strength = gr.Slider(
+                        label="Audio Helper strength",
+                        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,
+                )
+
+        with gr.Column():
+            output_video = gr.Video(label="Generated Video", autoplay=False)
+            gpu_duration = gr.Slider(
+                label="ZeroGPU duration (seconds; 10 second Img2Vid with 1024x1024 and LoRAs = ~70)",
+                minimum=30.0,
+                maximum=240.0,
+                value=75.0,
+                step=1.0,
+            )
+
+    gr.Examples(
+        examples=[
+            [
+                None,
+                "pinkknit.jpg",
+                None,
+                "The camera falls downward through darkness as if dropped into a tunnel. "
+                "As it slows, five friends wearing pink knitted hats and sunglasses lean "
+                "over and look down toward the camera with curious expressions. The lens "
+                "has a strong fisheye effect, creating a circular frame around them. They "
+                "crowd together closely, forming a symmetrical cluster while staring "
+                "directly into the lens.",
+                3.0,
+                80.0,
+                False,
+                42,
+                True,
+                1024,
+                1024,
+                0.0,  # pose_strength (example)
+                0.0,  # general_strength (example)
+                0.0,  # motion_strength (example)
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+            ],
+        ],
+        inputs=[
+            first_image, last_image, input_audio, prompt, duration, gpu_duration,
+            enhance_prompt, seed, randomize_seed, height, width,
+            pose_strength, general_strength, motion_strength, dreamlay_strength, mself_strength, dramatic_strength, fluid_strength, liquid_strength, demopose_strength,
+        ],
+    )
+
+    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, general_strength, motion_strength, dreamlay_strength, mself_strength, dramatic_strength, fluid_strength, liquid_strength, demopose_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,
+            pose_strength, general_strength, motion_strength, dreamlay_strength, mself_strength, dramatic_strength, fluid_strength, liquid_strength, demopose_strength,
+        ],
+        outputs=[output_video, seed],
+    )
+
+
+css = """
+.fillable{max-width: 1200px !important}
+"""
+
+if __name__ == "__main__":
+    demo.launch(theme=gr.themes.Citrus(), css=css)