Delete app(old).py

app(old).py

@@ -1,652 +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 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
-# >>> ADD these imports (place immediately after your video_vae import)
-from ltx_core.loader import LoraPathStrengthAndSDOps, LTXV_LORA_COMFY_RENAMING_MAP
-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
-
-# 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)
-
-# >>> ADD: download and prepare LoRA descriptor
-print("Downloading LoRA for this Space (dagloop5/LoRA:LoRA2.safetensors)...")
-lora_path = hf_hub_download(repo_id="dagloop5/LoRA", filename="pose_enhancer.safetensors")
-# Create a descriptor object that the LTX loader expects.
-# initial strength is set to 1.0; we'll mutate `.strength` at runtime from the UI slider.
-lora_descriptor = LoraPathStrengthAndSDOps(lora_path, 1.0, LTXV_LORA_COMFY_RENAMING_MAP)
-
-print(f"LoRA: {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
-pipeline = LTX23DistilledA2VPipeline(
-    distilled_checkpoint_path=checkpoint_path,
-    spatial_upsampler_path=spatial_upsampler_path,
-    gemma_root=gemma_root,
-    loras=[lora_descriptor],
-    quantization=QuantizationPolicy.fp8_cast(),
-)
-
-# Preload all models for ZeroGPU tensor packing.
-# >>> REPLACE the "Preload all models" block with this one:
-print("Preloading models (pinning decoders/encoders but leaving transformer dynamic)...")
-ledger = pipeline.model_ledger
-
-# NOTE: do NOT call ledger.transformer() here. We keep the transformer's construction dynamic
-# so that changes to lora_descriptor.strength (made at runtime) are applied when the transformer
-# is built. We DO preload other components that are safe to pin.
-_video_encoder = ledger.video_encoder()
-_video_decoder = ledger.video_decoder()
-_audio_encoder = ledger.audio_encoder()
-_audio_decoder = ledger.audio_decoder()
-_vocoder = ledger.vocoder()
-_spatial_upsampler = ledger.spatial_upsampler()
-_text_encoder = ledger.text_encoder()
-_embeddings_processor = ledger.gemma_embeddings_processor()
-
-# Replace ledger methods to return the pinned objects for those components.
-# Intentionally do NOT override ledger.transformer so transformer is built when needed.
-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("Selected models pinned. Transformer remains dynamic to reflect runtime LoRA strength.")
-print("Preload complete.")
-
-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,
-    lora_strength: float = 1.0,
-    progress=gr.Progress(track_tqdm=True),
-):
-    try:
-        global pipeline   # <<< ADD THIS LINE HERE (VERY TOP of try block)
-        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)
-
-        # >>> RUNTIME LoRA application (robust, multi-fallback)
-        # We cannot rely on mutating the original descriptor (some implementations are immutable),
-        # so create a fresh runtime descriptor and try multiple ways to install it.
-        runtime_strength = float(lora_strength)
-        replaced = False
-
-        # 1) Try simple approach: build a new LoraPathStrengthAndSDOps
-        runtime_lora = LoraPathStrengthAndSDOps(lora_path, runtime_strength, LTXV_LORA_COMFY_RENAMING_MAP)
-        print(f"[LoRA] attempting to apply runtime LoRA (strength={runtime_strength})")
-
-        # Try a few likely places to replace the descriptor used by the pipeline/ledger.
-        try:
-            # common attribute on pipeline
-            if hasattr(pipeline, "loras"):
-                try:
-                    pipeline.loras = [runtime_lora]
-                    replaced = True
-                    print("[LoRA] replaced pipeline.loras")
-                except Exception as e:
-                    print(f"[LoRA] pipeline.loras assignment failed: {e}")
-        except Exception:
-            pass
-
-        try:
-            # common attribute on the model ledger
-            if hasattr(pipeline, "model_ledger") and hasattr(pipeline.model_ledger, "loras"):
-                try:
-                    pipeline.model_ledger.loras = [runtime_lora]
-                    replaced = True
-                    print("[LoRA] replaced pipeline.model_ledger.loras")
-                except Exception as e:
-                    print(f"[LoRA] pipeline.model_ledger.loras assignment failed: {e}")
-        except Exception:
-            pass
-
-        try:
-            # some internals use a private _loras list
-            if hasattr(pipeline, "model_ledger") and hasattr(pipeline.model_ledger, "_loras"):
-                try:
-                    pipeline.model_ledger._loras = [runtime_lora]
-                    replaced = True
-                    print("[LoRA] replaced pipeline.model_ledger._loras")
-                except Exception as e:
-                    print(f"[LoRA] pipeline.model_ledger._loras assignment failed: {e}")
-        except Exception:
-            pass
-
-        # 2) If we succeeded replacing the descriptor in-place, clear transformer cache so it will rebuild
-        if replaced:
-            try:
-                if hasattr(pipeline.model_ledger, "_transformer"):
-                    pipeline.model_ledger._transformer = None
-                # also clear potential caches named similar to 'transformer_cache' if present
-                if hasattr(pipeline.model_ledger, "transformer_cache"):
-                    try:
-                        pipeline.model_ledger.transformer_cache = {}
-                    except Exception:
-                        pass
-                print("[LoRA] in-place descriptor replacement done; transformer cache cleared")
-            except Exception as e:
-                print(f"[LoRA] replacement succeeded but cache clearing failed: {e}")
-
-        # 3) FINAL FALLBACK - if none of the in-place replacements worked, rebuild the pipeline
-        if not replaced:
-            print("[LoRA] in-place replacement FAILED; rebuilding pipeline with runtime LoRA (this is slow)")
-            try:
-                # Rebuild pipeline object with the new LoRA descriptor
-                # NOTE: this replaces the global `pipeline`. We must declare global to reassign it.
-                pipeline = LTX23DistilledA2VPipeline(
-                    distilled_checkpoint_path=checkpoint_path,
-                    spatial_upsampler_path=spatial_upsampler_path,
-                    gemma_root=gemma_root,
-                    loras=[runtime_lora],
-                    quantization=QuantizationPolicy.fp8_cast(),
-                )
-
-                # After rebuilding, we *do not* re-run the original module-level preloads here,
-                # because re-pinning may be complex; the rebuilt pipeline will construct its
-                # own ledger as part of the first call. This is slower but reliable.
-                # Clear any transformer caches if they exist on the new ledger as well.
-                try:
-                    if hasattr(pipeline.model_ledger, "_transformer"):
-                        pipeline.model_ledger._transformer = None
-                except Exception:
-                    pass
-
-                print("[LoRA] pipeline rebuilt with runtime LoRA")
-            except Exception as e:
-                print(f"[LoRA] pipeline rebuild FAILED: {e}")
-
-        # Finally, log memory then proceed
-        log_memory("before pipeline call")
-
-        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=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)
-                
-                # >>> MOVE slider OUTSIDE the row
-                lora_strength = gr.Slider(
-                    label="LoRA Strength",
-                    info="Scale for the LoRA weights (0.0 = off). Set near 1.0 for full effect.",
-                    minimum=0.0,
-                    maximum=2.0,
-                    value=1.0,
-                    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,
-                1.0,
-            ],
-        ],
-        inputs=[
-            first_image, last_image, input_audio, prompt, duration,
-            enhance_prompt, seed, randomize_seed, height, width, lora_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, lora_strength
-        ],
-        outputs=[output_video, seed],
-    )
-
-
-css = """
-.fillable{max-width: 1200px !important}
-"""
-
-if __name__ == "__main__":
-    demo.launch(theme=gr.themes.Citrus(), css=css)