Delete app(theonethatreallyworks).py

app(theonethatreallyworks).py

@@ -1,723 +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"
-
-# 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")
-
-if not os.path.exists(LTX_REPO_DIR):
-    print(f"Cloning {LTX_REPO_URL}...")
-    subprocess.run(["git", "clone", "--depth", "1", LTX_REPO_URL, 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 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 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)},
-    "low": {"16:9": (768, 512), "9:16": (512, 768), "1:1": (768, 768)},
-}
-
-
-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 ="rahul7star/gemma-3-12b-it-heretic"
-
-
-# Download model checkpoints
-print("=" * 80)
-print("Downloading LTX-2.3 distilled model + Gemma...")
-print("=" * 80)
-
-checkpoint_path = hf_hub_download(repo_id=LTX_MODEL_REPO, filename="ltx-2.3-22b-distilled.safetensors")
-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="pose_enhancer.safetensors")
-general_lora_path = hf_hub_download(repo_id=LORA_REPO, filename="general_enhancer.safetensors")
-motion_lora_path = hf_hub_download(repo_id=LORA_REPO, filename="motion_helper.safetensors")
-
-print(f"Pose LoRA: {pose_lora_path}")
-print(f"General LoRA: {general_lora_path}")
-print(f"Motion LoRA: {motion_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 apply_loras_to_pipeline(pose_strength: float, general_strength: float, motion_strength: float):
-    """
-    Apply LoRAs by:
-      1) creating a temporary ledger with requested LoRAs,
-      2) building the fused transformer on CPU only,
-      3) copying parameters & buffers in-place into the existing GPU transformer,
-      4) freeing CPU objects and clearing cache.
-    This avoids having two full transformers on GPU simultaneously.
-    """
-    ledger = pipeline.model_ledger
-
-    entries = [
-        (pose_lora_path, float(pose_strength)),
-        (general_lora_path, float(general_strength)),
-        (motion_lora_path, float(motion_strength)),
-    ]
-
-    # Build LoraPathStrengthAndSDOps for non-zero strengths
-    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 len(loras_for_builder) == 0:
-        print("[LoRA] No nonzero LoRA strengths — skipping rebuild.")
-        return
-
-    try:
-        # Create temporary ledger configured with LoRAs
-        tmp_ledger = ledger.with_loras(tuple(loras_for_builder))
-        print(f"[LoRA] Built temporary ledger with {len(loras_for_builder)} LoRA(s).")
-
-        # Force the temporary ledger to build on CPU so the fused model is built on CPU.
-        # Save original attributes to restore them later.
-        orig_tmp_target = getattr(tmp_ledger, "_target_device", None)
-        orig_tmp_device = getattr(tmp_ledger, "device", None)
-        try:
-            # _target_device is expected to be callable by model_ledger.transformer()
-            # set it to a callable that returns CPU so builder.build(device=...) works.
-            tmp_ledger._target_device = (lambda: torch.device("cpu"))
-            # ledger.device is used after build: set it to CPU so .to(self.device) keeps the model on CPU.
-            tmp_ledger.device = torch.device("cpu")
-            print("[LoRA] Building fused transformer on CPU (no GPU allocation)...")
-            new_transformer_cpu = tmp_ledger.transformer()  # should now return a CPU model
-            print("[LoRA] Fused transformer built on CPU.")
-        finally:
-            # Restore attributes to their previous values (if there were any).
-            if orig_tmp_target is not None:
-                tmp_ledger._target_device = orig_tmp_target
-            else:
-                # remove attribute if ledger did not have it previously
-                try:
-                    delattr(tmp_ledger, "_target_device")
-                except Exception:
-                    pass
-            if orig_tmp_device is not None:
-                tmp_ledger.device = orig_tmp_device
-            else:
-                try:
-                    delattr(tmp_ledger, "device")
-                except Exception:
-                    pass
-
-        # Get the existing transformer instance (the one currently used by the pipeline).
-        global _transformer
-        try:
-            existing_transformer = _transformer
-        except NameError:
-            # If not cached, ask ledger for it (this will be the GPU-resident model already loaded).
-            existing_transformer = ledger.transformer()
-            _transformer = existing_transformer
-
-        # Map existing parameters & buffers for quick lookup
-        existing_params = {name: param for name, param in existing_transformer.named_parameters()}
-        existing_buffers = {name: buf for name, buf in existing_transformer.named_buffers()}
-
-        # State dict of CPU model (fused with LoRAs)
-        new_state = new_transformer_cpu.state_dict()
-        # diagnostics: how many keys will be copied
-        total_keys = len(new_state)
-        matched = sum(1 for k in new_state if k in existing_params or k in existing_buffers)
-        print(f"[LoRA] Transformer state keys: total={total_keys} matched_for_copy={matched}")
-        if matched == 0:
-            # helpful hint if naming differs
-            sample_keys = list(new_state.keys())[:10]
-            print(f"[LoRA] Warning: 0 matching keys found. sample new_state keys: {sample_keys}")
-
-        # Copy CPU tensors into the GPU-resident transformer's params/buffers in-place
-        with torch.no_grad():
-            for k, v in new_state.items():
-                if k in existing_params:
-                    tgt = existing_params[k].data
-                    try:
-                        tgt.copy_(v.to(tgt.device))
-                    except Exception as e:
-                        print(f"[LoRA] Failed to copy parameter {k}: {type(e).__name__}: {e}")
-                elif k in existing_buffers:
-                    tgt = existing_buffers[k].data
-                    try:
-                        tgt.copy_(v.to(tgt.device))
-                    except Exception as e:
-                        print(f"[LoRA] Failed to copy buffer {k}: {type(e).__name__}: {e}")
-                else:
-                    # Parameter name mismatch — skip
-                    # This can happen if LoRA changes expected keys; not fatal.
-                    # Print debug once for the first few unmatched keys.
-                    pass
-
-        # Free CPU-built transformer and temporary ledger resources, then clear caches
-        try:
-            del new_transformer_cpu
-            del tmp_ledger
-        except Exception:
-            pass
-        gc.collect()
-        torch.cuda.empty_cache()
-
-        print("[LoRA] In-place parameter copy complete. LoRAs applied to the existing transformer.")
-        return
-
-    except Exception as e:
-        import traceback
-        print(f"[LoRA] Error during in-place LoRA application: {type(e).__name__}: {e}")
-        print(traceback.format_exc())
-
-    # If something unexpectedly failed, bail out (no fallback).
-    print("[LoRA] apply_loras_to_pipeline finished (LOADING FAILED — no changes applied).")
-
-# ---- 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)
-
-
-@spaces.GPU(duration=80)
-@torch.inference_mode()
-def generate_video(
-    first_image,
-    last_image,
-    input_audio,
-    prompt: str,
-    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,
-    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_pipeline(pose_strength, general_strength, motion_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 Heretic Distilled") as demo:
-    gr.Markdown("# LTX-2.3 F2LF:Heretic 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=10.0, value=3.0, 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)")
-                    pose_strength = gr.Slider(
-                        label="Pose Enhancer strength",
-                        minimum=0.0, maximum=2.0, value=0.0, step=0.01
-                    )
-                    general_strength = gr.Slider(
-                        label="General Enhancer strength",
-                        minimum=0.0, maximum=2.0, value=0.0, step=0.01
-                    )
-                    motion_strength = gr.Slider(
-                        label="Motion Helper 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)
-
-    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,
-                False,
-                42,
-                True,
-                1024,
-                1024,
-                0.0,  # pose_strength (example)
-                0.0,  # general_strength (example)
-                0.0,  # motion_strength (example)
-            ],
-        ],
-        inputs=[
-            first_image, last_image, input_audio, prompt, duration,
-            enhance_prompt, seed, randomize_seed, height, width,
-            pose_strength, general_strength, motion_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, enhance_prompt,
-            seed, randomize_seed, height, width,
-            pose_strength, general_strength, motion_strength,
-        ],
-        outputs=[output_video, seed],
-    )
-
-
-css = """
-.fillable{max-width: 1200px !important}
-"""
-
-if __name__ == "__main__":
-    demo.launch(theme=gr.themes.Citrus(), css=css)