Upload app(theonethatreallyworks).py

app(theonethatreallyworks).py

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+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)