app(WIP).py

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 safetensors import safe_open
import json
import requests

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_audio_video,
    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)},
    "low": {"16:9": (768, 512), "9:16": (512, 768), "1:1": (768, 768)},
}


class LTX23DistilledA2VPipeline(DistilledPipeline):
    """DistilledPipeline: single stage, full resolution, 8 steps, with optional audio."""

    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,
    ):
        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,
        )
        video_context, audio_context = ctx_p.video_encoding, ctx_p.audio_encoding

        # Audio encoding — only runs if audio is provided
        encoded_audio_latent = None
        original_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)

            original_audio = Audio(
                waveform=decoded_audio.waveform.squeeze(0),
                sampling_rate=decoded_audio.sampling_rate,
            )

        video_encoder = self.model_ledger.video_encoder()
        transformer = self.model_ledger.transformer()
        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,
                ),
            )

        output_shape = VideoPixelShape(
            batch=1,
            frames=num_frames,
            width=width,
            height=height,
            fps=frame_rate,
        )
        conditionings = combined_image_conditionings(
            images=images,
            height=output_shape.height,
            width=output_shape.width,
            video_encoder=video_encoder,
            dtype=dtype,
            device=self.device,
        )
        video_state, audio_state = denoise_audio_video(
            output_shape=output_shape,
            conditionings=conditionings,
            noiser=noiser,
            sigmas=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()
        del transformer
        del video_encoder
        cleanup_memory()

        decoded_video = vae_decode_video(
            video_state.latent,
            self.model_ledger.video_decoder(),
            tiling_config,
            generator,
        )

        # If audio was provided as input, return it as-is (higher fidelity than decoded)
        # If no audio input, decode the generated audio latent from the denoising
        if original_audio is not None:
            return decoded_video, original_audio
        else:
            from ltx_core.model.audio_vae import decode_audio as vae_decode_audio
            generated_audio = vae_decode_audio(
                audio_state.latent,
                self.model_ledger.audio_decoder(),
                self.model_ledger.vocoder(),
            )
            return decoded_video, generated_audio


# Model repos
LTX_MODEL_REPO = "Lightricks/LTX-2.3"
GEMMA_REPO ="Lightricks/gemma-3-12b-it-qat-q4_0-unquantized"
GEMMA_ABLITERATED_REPO = "Sikaworld1990/gemma-3-12b-it-abliterated-sikaworld-high-fidelity-edition-Ltx-2"
GEMMA_ABLITERATED_FILE = "gemma-3-12b-it-abliterated-sikaworld-high-fidelity-edition.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

weights_dir = Path("weights")
weights_dir.mkdir(exist_ok=True)
checkpoint_path = hf_hub_download(
    repo_id=LTX_MODEL_REPO,
    filename="ltx-2.3-22b-distilled-1.1.safetensors",
    local_dir=str(weights_dir),
    local_dir_use_symlinks=False,
)
spatial_upsampler_path = hf_hub_download(repo_id=LTX_MODEL_REPO, filename="ltx-2.3-spatial-upscaler-x2-1.0.safetensors")

print("[Gemma] Setting up abliterated Gemma text encoder...")
MERGED_WEIGHTS = "/tmp/abliterated_gemma_merged.safetensors"
gemma_root = "/tmp/abliterated_gemma"
os.makedirs(gemma_root, exist_ok=True)

gemma_official_dir = snapshot_download(
    repo_id=GEMMA_REPO,
    ignore_patterns=["*.safetensors", "*.safetensors.index.json"],
)

for fname in os.listdir(gemma_official_dir):
    src = os.path.join(gemma_official_dir, fname)
    dst = os.path.join(gemma_root, fname)
    if os.path.isfile(src) and not fname.endswith(".safetensors") and fname != "model.safetensors.index.json":
        if not os.path.exists(dst):
            os.symlink(src, dst)

if os.path.exists(MERGED_WEIGHTS):
    print("[Gemma] Using cached merged weights")
else:
    abliterated_weights_path = hf_hub_download(
        repo_id=GEMMA_ABLITERATED_REPO,
        filename=GEMMA_ABLITERATED_FILE,
    )
    index_path = hf_hub_download(
        repo_id=GEMMA_REPO,
        filename="model.safetensors.index.json"
    )
    with open(index_path) as f:
        weight_index = json.load(f)

    vision_keys = {}
    for key, shard in weight_index["weight_map"].items():
        if "vision_tower" in key or "multi_modal_projector" in key:
            vision_keys[key] = shard
    needed_shards = set(vision_keys.values())

    shard_paths = {}
    for shard_name in needed_shards:
        shard_paths[shard_name] = hf_hub_download(
            repo_id=GEMMA_REPO,
            filename=shard_name
        )

    _fp8_types = {torch.float8_e4m3fn, torch.float8_e5m2}
    raw = load_file(abliterated_weights_path)
    merged = {}
    for key, tensor in raw.items():
        t = tensor.to(torch.bfloat16) if tensor.dtype in _fp8_types else tensor
        merged[f"language_model.{key}"] = t
    del raw

    for key, shard_name in vision_keys.items():
        with safe_open(shard_paths[shard_name], framework="pt") as f:
            merged[key] = f.get_tensor(key)

    save_file(merged, MERGED_WEIGHTS)
    del merged
    gc.collect()

weight_link = os.path.join(gemma_root, "model.safetensors")
if os.path.exists(weight_link):
    os.remove(weight_link)
os.symlink(MERGED_WEIGHTS, weight_link)
print(f"[Gemma] Root ready: {gemma_root}")

# ---- 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_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") # 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=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="FurryenhancerLTX2.3V1.215.safetensors")
transition_lora_path = hf_hub_download(repo_id="valiantcat/LTX-2.3-Transition-LORA", filename="ltx2.3-transition.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"Voice LoRA: {voice_lora_path}")
print(f"Realism LoRA: {realism_lora_path}")
print(f"Transition LoRA: {transition_lora_path}")
# ----------------------------------------------------------------

print(f"Spatial upsampler: {spatial_upsampler_path}")
print(f"Checkpoint: {checkpoint_path}")

# 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
)
# ----------------------------------------------------------------

# Currently applied LoRA deltas — stored so they can be undone before re-applying
_applied_lora_deltas: dict[str, torch.Tensor] = {}
_applied_lora_config: list[tuple[str, float]] = []


def _load_and_rename_lora_tensors(lora_path: str) -> dict[str, torch.Tensor]:
    """Load LoRA tensors from disk and apply ComfyUI→LTX key renaming."""
    tensors = {}
    with safe_open(lora_path, framework="pt", device="cpu") as f:
        for key in f.keys():
            tensors[key] = f.get_tensor(key)

    renamed = {}
    for key, tensor in tensors.items():
        new_key = key
        for old_substr, new_substr in LTXV_LORA_COMFY_RENAMING_MAP.items():
            new_key = new_key.replace(old_substr, new_substr)
        renamed[new_key] = tensor

    return renamed


def _compute_lora_deltas(lora_path: str, strength: float) -> dict[str, torch.Tensor]:
    """Compute weight delta tensors for a single LoRA at given strength."""
    tensors = _load_and_rename_lora_tensors(lora_path)
    deltas = {}

    # Collect all base keys that have a down component
    base_keys = set()
    for key in tensors:
        for suffix in [".lora_down.weight", ".lora_A.weight"]:
            if key.endswith(suffix):
                base_keys.add(key[: -len(suffix)])

    for base in base_keys:
        down = tensors.get(base + ".lora_down.weight") or tensors.get(base + ".lora_A.weight")
        up = tensors.get(base + ".lora_up.weight") or tensors.get(base + ".lora_B.weight")

        if down is None or up is None:
            continue

        alpha_val = tensors.get(base + ".alpha")
        scale = (alpha_val.item() / down.shape[0]) if alpha_val is not None else 1.0

        down_f = down.float()
        up_f = up.float()

        if down_f.dim() == 2 and up_f.dim() == 2:
            delta = up_f @ down_f
        elif down_f.dim() == 4:
            delta = (up_f.flatten(1) @ down_f.flatten(1)).view(
                up_f.shape[0], down_f.shape[1], *up_f.shape[2:]
            )
        else:
            print(f"[LoRA] Skipping {base}: unexpected dims down={down_f.dim()} up={up_f.dim()}")
            continue

        deltas[base + ".weight"] = (delta * strength * scale).to(torch.bfloat16)

    return deltas


def apply_loras_to_transformer(
    pose_strength, general_strength, motion_strength, dreamlay_strength,
    mself_strength, dramatic_strength, fluid_strength, liquid_strength,
    demopose_strength, voice_strength, realism_strength, transition_strength,
):
    global _applied_lora_deltas, _applied_lora_config

    lora_configs = [
        (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)),
        (voice_lora_path,      round(float(voice_strength), 2)),
        (realism_lora_path,    round(float(realism_strength), 2)),
        (transition_lora_path, round(float(transition_strength), 2)),
    ]

    # Skip if config hasn't changed since last application
    if lora_configs == _applied_lora_config:
        print("[LoRA] Config unchanged, skipping re-application.")
        return

    # Undo previously applied deltas
    if _applied_lora_deltas:
        print(f"[LoRA] Undoing {len(_applied_lora_deltas)} previously applied delta(s)...")
        with torch.no_grad():
            for name, param in _transformer.named_parameters():
                if name in _applied_lora_deltas:
                    param.data -= _applied_lora_deltas[name].to(
                        device=param.device, dtype=param.dtype
                    )
        _applied_lora_deltas = {}
        gc.collect()

    active = [(p, s) for p, s in lora_configs if p is not None and s != 0.0]
    if not active:
        print("[LoRA] No active LoRAs.")
        _applied_lora_config = lora_configs
        return

    print(f"[LoRA] Computing deltas for {len(active)} active LoRA(s)...")
    combined_deltas: dict[str, torch.Tensor] = {}
    for lora_path, strength in active:
        try:
            deltas = _compute_lora_deltas(lora_path, strength)
            for key, delta in deltas.items():
                if key in combined_deltas:
                    combined_deltas[key] = combined_deltas[key] + delta
                else:
                    combined_deltas[key] = delta
            print(f"[LoRA] {Path(lora_path).name}: {len(deltas)} delta(s) at strength {strength}")
        except Exception as e:
            import traceback
            print(f"[LoRA] Failed on {lora_path}: {e}\n{traceback.format_exc()}")

    applied_count = 0
    with torch.no_grad():
        for name, param in _transformer.named_parameters():
            if name in combined_deltas:
                param.data += combined_deltas[name].to(
                    device=param.device, dtype=param.dtype
                )
                applied_count += 1

    _applied_lora_deltas = combined_deltas
    _applied_lora_config = lora_configs
    print(f"[LoRA] Applied {applied_count} weight delta(s) to live transformer.")
    gc.collect()

# ---- 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,
    voice_strength: float = 0.0,
    realism_strength: float = 0.0,
    transition_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,
    voice_strength: float = 0.0,
    realism_strength: float = 0.0,
    transition_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_loras_to_transformer(
            pose_strength, general_strength, motion_strength, dreamlay_strength,
            mself_strength, dramatic_strength, fluid_strength, liquid_strength,
            demopose_strength, voice_strength, realism_strength, transition_strength,
        )

        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="Liquid 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
                    )
                    voice_strength = gr.Slider(
                        label="Voice Helper strength",
                        minimum=0.0, maximum=2.0, value=0.0, step=0.01
                    )
                    realism_strength = gr.Slider(
                        label="Anthro Realism strength",
                        minimum=0.0, maximum=2.0, value=0.0, step=0.01
                    )
                    transition_strength = gr.Slider(
                        label="Transition strength",
                        minimum=0.0, maximum=2.0, value=0.0, step=0.01
                    )

        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,
                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, voice_strength, realism_strength, transition_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],
    )
    
    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, voice_strength, realism_strength, transition_strength,
        ],
        outputs=[output_video, seed],
    )


css = """
.fillable{max-width: 1200px !important}
"""

if __name__ == "__main__":
    demo.launch(theme=gr.themes.Citrus(), css=css)