Delete app.py

app.py

@@ -1,611 +0,0 @@
-# =============================================================================
-# Installation and Setup
-# =============================================================================
-import os
-import subprocess
-import sys
-
-os.environ["TORCH_COMPILE_DISABLE"] = "1"
-os.environ["TORCHDYNAMO_DISABLE"] = "1"
-
-# Clone LTX-2 repo at the commit with ModelLedger-based HQ pipeline
-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_SHA = "ae855f8538843825f9015a419cf4ba5edaf5eec2"
-
-if not os.path.exists(LTX_REPO_DIR):
-    print(f"Cloning {LTX_REPO_URL} at commit {LTX_COMMIT_SHA}...")
-    os.makedirs(LTX_REPO_DIR)
-    subprocess.run(["git", "init", LTX_REPO_DIR], check=True)
-    subprocess.run(["git", "remote", "add", "origin", LTX_REPO_URL], cwd=LTX_REPO_DIR, check=True)
-    subprocess.run(["git", "fetch", "--depth", "1", "origin", LTX_COMMIT_SHA], cwd=LTX_REPO_DIR, check=True)
-    subprocess.run(["git", "checkout", LTX_COMMIT_SHA], cwd=LTX_REPO_DIR, 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"))
-
-# =============================================================================
-# Imports
-# =============================================================================
-import logging
-import random
-import tempfile
-from pathlib import Path
-
-import torch
-torch._dynamo.config.suppress_errors = True
-torch._dynamo.config.disable = True
-
-import gradio as gr
-import spaces
-import numpy as np
-from huggingface_hub import hf_hub_download, snapshot_download
-
-# Core LTX imports from the ModelLedger-based HQ pipeline
-from ltx_core.model.video_vae import TilingConfig, get_video_chunks_number
-from ltx_core.quantization import QuantizationPolicy
-from ltx_core.loader import LoraPathStrengthAndSDOps, LTXV_LORA_COMFY_RENAMING_MAP
-from ltx_core.components.guiders import MultiModalGuiderParams
-
-# Import the HQ pipeline with ModelLedger
-from ltx_pipelines.ti2vid_two_stages_hq import TI2VidTwoStagesHQPipeline
-
-# Import constants
-from ltx_pipelines.utils.constants import LTX_2_3_HQ_PARAMS
-
-logging.getLogger().setLevel(logging.INFO)
-
-# =============================================================================
-# Constants
-# =============================================================================
-
-LTX_MODEL_REPO = "Lightricks/LTX-2.3"
-GEMMA_REPO = "Lightricks/gemma-3-12b-it-qat-q4_0-unquantized"
-DEFAULT_FRAME_RATE = 24.0
-MIN_DIM, MAX_DIM, STEP = 256, 1280, 64
-MIN_FRAMES, MAX_FRAMES = 9, 257
-MAX_SEED = np.iinfo(np.int32).max
-
-DEFAULT_PROMPT = (
-    "A majestic eagle soaring over mountain peaks at sunset, "
-    "wings spread wide against the orange sky, feathers catching the light, "
-    "wind currents visible in the motion blur, cinematic slow motion, 4K quality"
-)
-DEFAULT_NEGATIVE_PROMPT = (
-    "worst quality, inconsistent motion, blurry, jittery, distorted, "
-    "deformed, artifacts, text, watermark, logo, frame, border, "
-    "low resolution, pixelated, unnatural, fake, CGI, cartoon"
-)
-
-# =============================================================================
-# Model Download
-# =============================================================================
-
-print("=" * 80)
-print("Downloading LTX-2.3 models...")
-print("=" * 80)
-
-checkpoint_path = hf_hub_download(repo_id=LTX_MODEL_REPO, filename="ltx-2.3-22b-dev.safetensors")
-spatial_upsampler_path = hf_hub_download(repo_id=LTX_MODEL_REPO, filename="ltx-2.3-spatial-upscaler-x2-1.1.safetensors")
-distilled_lora_path = hf_hub_download(repo_id=LTX_MODEL_REPO, filename="ltx-2.3-22b-distilled-lora-384.safetensors")
-gemma_root = snapshot_download(repo_id=GEMMA_REPO)
-
-print(f"Dev checkpoint: {checkpoint_path}")
-print(f"Spatial upsampler: {spatial_upsampler_path}")
-print(f"Distilled LoRA: {distilled_lora_path}")
-print(f"Gemma root: {gemma_root}")
-print("=" * 80)
-
-# =============================================================================
-# Pipeline Initialization
-# =============================================================================
-
-print("Initializing TI2VidTwoStagesHQPipeline with ModelLedger...")
-
-# Create LoRA configuration for distilled model
-distilled_lora = [
-    LoraPathStrengthAndSDOps(
-        path=distilled_lora_path,
-        strength=1.0,  # Will be set per-stage (0.25, 0.5)
-        sd_ops=LTXV_LORA_COMFY_RENAMING_MAP,
-    )
-]
-
-# Initialize the ModelLedger-based HQ pipeline
-pipeline = TI2VidTwoStagesHQPipeline(
-    checkpoint_path=checkpoint_path,
-    distilled_lora=distilled_lora,
-    distilled_lora_strength_stage_1=0.25,  # From HQ params
-    distilled_lora_strength_stage_2=0.50,  # From HQ params
-    spatial_upsampler_path=spatial_upsampler_path,
-    gemma_root=gemma_root,
-    loras=(),  # No additional custom LoRAs
-    quantization=QuantizationPolicy.fp8_cast(),
-)
-
-print("Pipeline initialized successfully!")
-print("=" * 80)
-
-# =============================================================================
-# ZeroGPU Tensor Preloading - ModelLedger Pattern
-# =============================================================================
-# This pipeline has TWO model ledgers: stage_1_model_ledger and stage_2_model_ledger
-# We must preload both for ZeroGPU tensor packing to work
-
-print("Preloading all models for ZeroGPU tensor packing...")
-print("This may take a few minutes...")
-
-# ===== Stage 1 Model Ledger =====
-print("  Loading Stage 1 models...")
-
-# Stage 1 transformer
-ledger1 = pipeline.stage_1_model_ledger
-_transformer_s1 = ledger1.transformer()
-ledger1.transformer = lambda: _transformer_s1
-
-# Stage 1 video encoder
-_video_encoder_s1 = ledger1.video_encoder()
-ledger1.video_encoder = lambda: _video_encoder_s1
-
-# Stage 1 video decoder
-_video_decoder_s1 = ledger1.video_decoder()
-ledger1.video_decoder = lambda: _video_decoder_s1
-
-# Stage 1 audio decoder
-_audio_decoder_s1 = ledger1.audio_decoder()
-ledger1.audio_decoder = lambda: _audio_decoder_s1
-
-# Stage 1 vocoder
-_vocoder_s1 = ledger1.vocoder()
-ledger1.vocoder = lambda: _vocoder_s1
-
-# Stage 1 spatial upsampler
-_spatial_upsampler_s1 = ledger1.spatial_upsampler()
-ledger1.spatial_upsampler = lambda: _spatial_upsampler_s1
-
-# Stage 1 text encoder (Gemma)
-_text_encoder_s1 = ledger1.text_encoder()
-ledger1.text_encoder = lambda: _text_encoder_s1
-
-# Stage 1 embeddings processor
-_embeddings_processor_s1 = ledger1.embeddings_processor()
-ledger1.embeddings_processor = lambda: _embeddings_processor_s1
-
-print("    Stage 1 models loaded")
-
-# ===== Stage 2 Model Ledger =====
-print("  Loading Stage 2 models...")
-
-# Stage 2 transformer
-ledger2 = pipeline.stage_2_model_ledger
-_transformer_s2 = ledger2.transformer()
-ledger2.transformer = lambda: _transformer_s2
-
-# Stage 2 video encoder
-_video_encoder_s2 = ledger2.video_encoder()
-ledger2.video_encoder = lambda: _video_encoder_s2
-
-# Stage 2 video decoder
-_video_decoder_s2 = ledger2.video_decoder()
-ledger2.video_decoder = lambda: _video_decoder_s2
-
-# Stage 2 audio decoder
-_audio_decoder_s2 = ledger2.audio_decoder()
-ledger2.audio_decoder = lambda: _audio_decoder_s2
-
-# Stage 2 vocoder
-_vocoder_s2 = ledger2.vocoder()
-ledger2.vocoder = lambda: _vocoder_s2
-
-# Stage 2 spatial upsampler
-_spatial_upsampler_s2 = ledger2.spatial_upsampler()
-ledger2.spatial_upsampler = lambda: _spatial_upsampler_s2
-
-# Stage 2 text encoder (Gemma) - can reuse from stage 1
-_text_encoder_s2 = ledger2.text_encoder()
-ledger2.text_encoder = lambda: _text_encoder_s2
-
-# Stage 2 embeddings processor - can reuse from stage 1
-_embeddings_processor_s2 = ledger2.embeddings_processor()
-ledger2.embeddings_processor = lambda: _embeddings_processor_s2
-
-print("    Stage 2 models loaded")
-
-# Create global references to prevent garbage collection
-# Module-level variables persist for ZeroGPU to pack
-_transformer_stage1 = _transformer_s1
-_transformer_stage2 = _transformer_s2
-_video_encoder_stage1 = _video_encoder_s1
-_video_encoder_stage2 = _video_encoder_s2
-_video_decoder_stage1 = _video_decoder_s1
-_video_decoder_stage2 = _video_decoder_s2
-_audio_decoder_stage1 = _audio_decoder_s1
-_audio_decoder_stage2 = _audio_decoder_s2
-_vocoder_stage1 = _vocoder_s1
-_vocoder_stage2 = _vocoder_s2
-_spatial_upsampler_stage1 = _spatial_upsampler_s1
-_spatial_upsampler_stage2 = _spatial_upsampler_s2
-_text_encoder_stage1 = _text_encoder_s1
-_text_encoder_stage2 = _text_encoder_s2
-_embeddings_processor_stage1 = _embeddings_processor_s1
-_embeddings_processor_stage2 = _embeddings_processor_s2
-
-print("All models preloaded for ZeroGPU tensor packing!")
-print("=" * 80)
-
-# =============================================================================
-# Helper Functions
-# =============================================================================
-
-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 calculate_frames(duration: float, frame_rate: float = DEFAULT_FRAME_RATE) -> int:
-    ideal_frames = int(duration * frame_rate)
-    ideal_frames = max(ideal_frames, MIN_FRAMES)
-    k = round((ideal_frames - 1) / 8)
-    frames = k * 8 + 1
-    return min(frames, MAX_FRAMES)
-
-
-def validate_resolution(height: int, width: int) -> tuple[int, int]:
-    height = round(height / STEP) * STEP
-    width = round(width / STEP) * STEP
-    height = max(MIN_DIM, min(height, MAX_DIM))
-    width = max(MIN_DIM, min(width, MAX_DIM))
-    return height, width
-
-
-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]))
-
-
-RESOLUTIONS = {
-    "16:9": {"width": 1280, "height": 704},
-    "9:16": {"width": 704, "height": 1280},
-    "1:1": {"width": 960, "height": 960},
-}
-
-
-def get_duration(
-    prompt: str,
-    negative_prompt: str,
-    input_image,
-    duration: float,
-    seed: int,
-    randomize_seed: bool,
-    height: int,
-    width: int,
-    enhance_prompt: bool,
-    video_cfg_scale: float,
-    video_stg_scale: float,
-    video_rescale_scale: float,
-    video_a2v_scale: float,
-    audio_cfg_scale: float,
-    audio_stg_scale: float,
-    audio_rescale_scale: float,
-    audio_v2a_scale: float,
-    progress,
-) -> int:
-    base = 60
-    if duration > 4:
-        base += 15
-    if duration > 6:
-        base += 15
-    if height > 700 or width > 1000:
-        base += 15
-    frames_from_duration = int(duration * DEFAULT_FRAME_RATE)
-    if frames_from_duration > 81:
-        base += 10
-    return min(base, 90)
-
-
-@spaces.GPU(duration=get_duration)
-@torch.inference_mode()
-def generate_video(
-    prompt: str,
-    negative_prompt: str,
-    input_image,
-    duration: float,
-    seed: int,
-    randomize_seed: bool,
-    height: int,
-    width: int,
-    enhance_prompt: bool,
-    video_cfg_scale: float,
-    video_stg_scale: float,
-    video_rescale_scale: float,
-    video_a2v_scale: float,
-    audio_cfg_scale: float,
-    audio_stg_scale: float,
-    audio_rescale_scale: float,
-    audio_v2a_scale: float,
-    progress=gr.Progress(track_tqdm=True),
-):
-    """
-    Generate high-quality video using the ModelLedger-based HQ pipeline.
-    
-    This pipeline uses ModelLedger (like DistilledPipeline) for ZeroGPU compatibility,
-    while supporting CFG/negative prompts (like original TI2VidTwoStagesHQPipeline).
-    """
-    try:
-        torch.cuda.reset_peak_memory_stats()
-        log_memory("start")
-
-        current_seed = random.randint(0, MAX_SEED) if randomize_seed else int(seed)
-        print(f"Using seed: {current_seed}")
-
-        height, width = validate_resolution(int(height), int(width))
-        print(f"Resolution: {width}x{height}")
-
-        num_frames = calculate_frames(duration, DEFAULT_FRAME_RATE)
-        print(f"Frames: {num_frames} ({duration}s @ {DEFAULT_FRAME_RATE}fps)")
-
-        # Prepare image conditioning if provided
-        images = []
-        if input_image is not None:
-            output_dir = Path("outputs")
-            output_dir.mkdir(exist_ok=True)
-            temp_image_path = output_dir / f"temp_input_{current_seed}.jpg"
-            if hasattr(input_image, "save"):
-                input_image.save(temp_image_path)
-            else:
-                import shutil
-                shutil.copy(input_image, temp_image_path)
-            # Use ImageConditioningInput format
-            images = [(str(temp_image_path), 1.0)]  # (path, strength)
-
-        tiling_config = TilingConfig.default()
-        video_chunks_number = get_video_chunks_number(num_frames, tiling_config)
-
-        # Configure MultiModalGuider parameters
-        video_guider_params = MultiModalGuiderParams(
-            cfg_scale=video_cfg_scale,
-            stg_scale=video_stg_scale,
-            rescale_scale=video_rescale_scale,
-            modality_scale=video_a2v_scale,
-            skip_step=0,
-            stg_blocks=[],
-        )
-
-        audio_guider_params = MultiModalGuiderParams(
-            cfg_scale=audio_cfg_scale,
-            stg_scale=audio_stg_scale,
-            rescale_scale=audio_rescale_scale,
-            modality_scale=audio_v2a_scale,
-            skip_step=0,
-            stg_blocks=[],
-        )
-
-        log_memory("before pipeline call")
-
-        # Call the pipeline
-        video, audio = pipeline(
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            seed=current_seed,
-            height=height,
-            width=width,
-            num_frames=num_frames,
-            frame_rate=DEFAULT_FRAME_RATE,
-            num_inference_steps=LTX_2_3_HQ_PARAMS.num_inference_steps,
-            video_guider_params=video_guider_params,
-            audio_guider_params=audio_guider_params,
-            images=images,
-            tiling_config=tiling_config,
-            enhance_prompt=enhance_prompt,
-        )
-
-        log_memory("after pipeline call")
-
-        # Encode video with audio
-        from ltx_pipelines.utils.media_io import encode_video
-        output_path = tempfile.mktemp(suffix=".mp4")
-        encode_video(
-            video=video,
-            fps=DEFAULT_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
-
-
-# =============================================================================
-# Gradio UI
-# =============================================================================
-
-css = """
-.fillable {max-width: 1200px !important}
-.progress-text {color: white}
-"""
-
-with gr.Blocks(title="LTX-2.3 Two-Stage HQ Video Generation") as demo:
-    gr.Markdown("# LTX-2.3 Two-Stage HQ Video Generation")
-    gr.Markdown(
-        "High-quality text/image-to-video generation using ModelLedger. "
-        "[[Model]](https://huggingface.co/Lightricks/LTX-2.3) "
-        "[[GitHub]](https://github.com/Lightricks/LTX-2)"
-    )
-
-    with gr.Row():
-        with gr.Column():
-            input_image = gr.Image(
-                label="Input Image (Optional - for image-to-video)",
-                type="pil",
-                sources=["upload", "webcam", "clipboard"]
-            )
-            
-            prompt = gr.Textbox(
-                label="Prompt",
-                info="Describe the video you want to generate",
-                value=DEFAULT_PROMPT,
-                lines=3,
-            )
-            
-            negative_prompt = gr.Textbox(
-                label="Negative Prompt",
-                info="What to avoid in the generated video",
-                value=DEFAULT_NEGATIVE_PROMPT,
-                lines=2,
-            )
-            
-            duration = gr.Slider(
-                label="Duration (seconds)",
-                minimum=0.5,
-                maximum=8.0,
-                value=2.0,
-                step=0.1,
-            )
-            
-            enhance_prompt = gr.Checkbox(
-                label="Enhance Prompt",
-                value=False,
-            )
-
-            generate_btn = gr.Button("Generate Video", variant="primary", size="lg")
-
-        with gr.Column():
-            output_video = gr.Video(
-                label="Generated Video",
-                autoplay=True,
-                interactive=False
-            )
-
-    with gr.Accordion("Advanced Settings", open=False):
-        with gr.Row():
-            width = gr.Number(label="Width", value=1280, precision=0)
-            height = gr.Number(label="Height", value=704, precision=0)
-        
-        with gr.Row():
-            seed = gr.Number(label="Seed", value=42, precision=0, minimum=0, maximum=MAX_SEED)
-            randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
-        
-        gr.Markdown("### Video Guidance Parameters")
-        gr.Markdown("Control how strongly the model follows the video prompt.")
-        
-        with gr.Row():
-            video_cfg_scale = gr.Slider(
-                label="Video CFG Scale",
-                minimum=1.0,
-                maximum=10.0,
-                value=LTX_2_3_HQ_PARAMS.video_guider_params.cfg_scale,
-                step=0.1,
-            )
-            video_stg_scale = gr.Slider(
-                label="Video STG Scale",
-                minimum=0.0,
-                maximum=2.0,
-                value=0.0,
-                step=0.1,
-            )
-        
-        with gr.Row():
-            video_rescale_scale = gr.Slider(
-                label="Video Rescale",
-                minimum=0.0,
-                maximum=2.0,
-                value=0.45,
-                step=0.1,
-            )
-            video_a2v_scale = gr.Slider(
-                label="A2V Scale",
-                minimum=0.0,
-                maximum=5.0,
-                value=3.0,
-                step=0.1,
-            )
-        
-        gr.Markdown("### Audio Guidance Parameters")
-        gr.Markdown("Control audio generation quality and sync.")
-        
-        with gr.Row():
-            audio_cfg_scale = gr.Slider(
-                label="Audio CFG Scale",
-                minimum=1.0,
-                maximum=15.0,
-                value=LTX_2_3_HQ_PARAMS.audio_guider_params.cfg_scale,
-                step=0.1,
-            )
-            audio_stg_scale = gr.Slider(
-                label="Audio STG Scale",
-                minimum=0.0,
-                maximum=2.0,
-                value=0.0,
-                step=0.1,
-            )
-        
-        with gr.Row():
-            audio_rescale_scale = gr.Slider(
-                label="Audio Rescale",
-                minimum=0.0,
-                maximum=2.0,
-                value=1.0,
-                step=0.1,
-            )
-            audio_v2a_scale = gr.Slider(
-                label="V2A Scale",
-                minimum=0.0,
-                maximum=5.0,
-                value=3.0,
-                step=0.1,
-            )
-
-    def on_image_upload(image, current_h, current_w):
-        if image is None:
-            return gr.update(), gr.update()
-        aspect = detect_aspect_ratio(image)
-        if aspect in RESOLUTIONS:
-            return (
-                gr.update(value=RESOLUTIONS[aspect]["width"]),
-                gr.update(value=RESOLUTIONS[aspect]["height"])
-            )
-        return gr.update(), gr.update()
-
-    input_image.change(
-        fn=on_image_upload,
-        inputs=[input_image, height, width],
-        outputs=[width, height],
-    )
-
-    generate_btn.click(
-        fn=generate_video,
-        inputs=[
-            prompt, negative_prompt, input_image, duration,
-            seed, randomize_seed, height, width, enhance_prompt,
-            video_cfg_scale, video_stg_scale, video_rescale_scale, video_a2v_scale,
-            audio_cfg_scale, audio_stg_scale, audio_rescale_scale, audio_v2a_scale,
-        ],
-        outputs=[output_video, seed],
-    )
-
-
-if __name__ == "__main__":
-    demo.queue().launch(
-        theme=gr.themes.Citrus(),
-        css=css,
-        mcp_server=True,
-    )