app(draft).py

import os
import subprocess
import sys

# Disable torch.compile / dynamo before any torch import
os.environ["TORCH_COMPILE_DISABLE"] = "1"
os.environ["TORCHDYNAMO_DISABLE"] = "1"

# Install xformers for memory-efficient attention
subprocess.run([sys.executable, "-m", "pip", "install", "xformers==0.0.32.post2", "--no-build-isolation"], check=False)

# Clone LTX-2 repo and install packages
LTX_REPO_URL = "https://github.com/Lightricks/LTX-2.git"
LTX_REPO_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "LTX-2")
LTX_COMMIT_SHA = "a2c3f24078eb918171967f74b6f66b756b29ee45"

if not os.path.exists(LTX_REPO_DIR):
    print(f"Cloning {LTX_REPO_URL}...")
    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)


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
from collections.abc import Iterator

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 Res2sDiffusionStep
from ltx_core.components.guiders import MultiModalGuider, MultiModalGuiderParams
from ltx_core.components.noisers import GaussianNoiser
from ltx_core.components.schedulers import LTX2Scheduler
from ltx_core.loader import LoraPathStrengthAndSDOps
from ltx_core.loader.registry import Registry
from ltx_core.model.video_vae import TilingConfig, get_video_chunks_number
from ltx_core.quantization import QuantizationPolicy
from ltx_core.types import Audio, VideoLatentShape, VideoPixelShape
from ltx_pipelines.utils.args import ImageConditioningInput, hq_2_stage_arg_parser
from ltx_pipelines.utils.blocks import (
    AudioDecoder,
    DiffusionStage,
    ImageConditioner,
    PromptEncoder,
    VideoDecoder,
    VideoUpsampler,
)
from ltx_pipelines.utils.constants import (
    LTX_2_3_HQ_PARAMS,
    STAGE_2_DISTILLED_SIGMAS,
)
from ltx_pipelines.utils.denoisers import GuidedDenoiser, SimpleDenoiser
from ltx_pipelines.utils.helpers import (
    assert_resolution,
    combined_image_conditionings,
    get_device,
)
from ltx_pipelines.utils.media_io import encode_video
from ltx_pipelines.utils.samplers import res2s_audio_video_denoising_loop
from ltx_pipelines.utils.types import ModalitySpec

# 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 TI2VidTwoStagesHQPipeline:
    """
    Two-stage text/image-to-video generation pipeline using the res_2s sampler.
    Same structure as :class:`TI2VidTwoStagesPipeline`: stage 1 generates video at
    half of the target resolution with CFG guidance (assuming  full model is used),
    then Stage 2 upsamples by 2x and refines using a distilled LoRA for higher
    quality output.
    Uses the res_2s second-order sampler instead of Euler, allowing fewer
    steps for comparable quality. Supports optional image conditioning via
    the images parameter.
    """

    def __init__(  # noqa: PLR0913
        self,
        checkpoint_path: str,
        distilled_lora: list[LoraPathStrengthAndSDOps],
        distilled_lora_strength_stage_1: float,
        distilled_lora_strength_stage_2: float,
        spatial_upsampler_path: str,
        gemma_root: str,
        loras: tuple[LoraPathStrengthAndSDOps, ...],
        device: torch.device | None = None,
        quantization: QuantizationPolicy | None = None,
        registry: Registry | None = None,
        torch_compile: bool = False,
    ):
        self.device = device or get_device()
        self.dtype = torch.bfloat16
        self._scheduler = LTX2Scheduler()

        distilled_lora_stage_1 = LoraPathStrengthAndSDOps(
            path=distilled_lora[0].path,
            strength=distilled_lora_strength_stage_1,
            sd_ops=distilled_lora[0].sd_ops,
        )
        distilled_lora_stage_2 = LoraPathStrengthAndSDOps(
            path=distilled_lora[0].path,
            strength=distilled_lora_strength_stage_2,
            sd_ops=distilled_lora[0].sd_ops,
        )

        self.prompt_encoder = PromptEncoder(checkpoint_path, gemma_root, self.dtype, self.device, registry=registry)
        self.image_conditioner = ImageConditioner(checkpoint_path, self.dtype, self.device, registry=registry)
        self.upsampler = VideoUpsampler(
            checkpoint_path, spatial_upsampler_path, self.dtype, self.device, registry=registry
        )
        self.video_decoder = VideoDecoder(checkpoint_path, self.dtype, self.device, registry=registry)
        self.audio_decoder = AudioDecoder(checkpoint_path, self.dtype, self.device, registry=registry)

        self.stage_1 = DiffusionStage(
            checkpoint_path,
            self.dtype,
            self.device,
            loras=(*loras, distilled_lora_stage_1),
            quantization=quantization,
            registry=registry,
            torch_compile=torch_compile,
        )
        self.stage_2 = DiffusionStage(
            checkpoint_path,
            self.dtype,
            self.device,
            loras=(*loras, distilled_lora_stage_2),
            quantization=quantization,
            registry=registry,
            torch_compile=torch_compile,
        )

    @torch.inference_mode()
    def __call__(  # noqa: PLR0913
        self,
        prompt: str,
        negative_prompt: str,
        seed: int,
        height: int,
        width: int,
        num_frames: int,
        frame_rate: float,
        num_inference_steps: int,
        video_guider_params: MultiModalGuiderParams,
        audio_guider_params: MultiModalGuiderParams,
        images: list[ImageConditioningInput],
        tiling_config: TilingConfig | None = None,
        enhance_prompt: bool = False,
        streaming_prefetch_count: int | None = None,
        max_batch_size: int = 1,
        stage_1_sigmas: torch.Tensor | None = None,
        stage_2_sigmas: torch.Tensor = STAGE_2_DISTILLED_SIGMAS,
    ) -> tuple[Iterator[torch.Tensor], Audio]:
        assert_resolution(height=height, width=width, is_two_stage=True)

        generator = torch.Generator(device=self.device).manual_seed(seed)
        noiser = GaussianNoiser(generator=generator)
        dtype = torch.bfloat16

        ctx_p, ctx_n = self.prompt_encoder(
            [prompt, negative_prompt],
            enhance_first_prompt=enhance_prompt,
            enhance_prompt_image=images[0][0] if len(images) > 0 else None,
            enhance_prompt_seed=seed,
            streaming_prefetch_count=streaming_prefetch_count,
        )
        v_context_p, a_context_p = ctx_p.video_encoding, ctx_p.audio_encoding
        v_context_n, a_context_n = ctx_n.video_encoding, ctx_n.audio_encoding

        # Stage 1: Generate video at half resolution with CFG guidance using res2s sampler.
        stage_1_output_shape = VideoPixelShape(
            batch=1,
            frames=num_frames,
            width=width // 2,
            height=height // 2,
            fps=frame_rate,
        )
        stage_1_conditionings = self.image_conditioner(
            lambda enc: combined_image_conditionings(
                images=images,
                height=stage_1_output_shape.height,
                width=stage_1_output_shape.width,
                video_encoder=enc,
                dtype=dtype,
                device=self.device,
            )
        )

        stepper = Res2sDiffusionStep()

        if stage_1_sigmas is None:
            empty_latent = torch.empty(VideoLatentShape.from_pixel_shape(stage_1_output_shape).to_torch_shape())
            stage_1_sigmas = self._scheduler.execute(latent=empty_latent, steps=num_inference_steps)
        sigmas = stage_1_sigmas.to(dtype=torch.float32, device=self.device)

        video_state, audio_state = self.stage_1(
            denoiser=GuidedDenoiser(
                v_context=v_context_p,
                a_context=a_context_p,
                video_guider=MultiModalGuider(
                    params=video_guider_params,
                    negative_context=v_context_n,
                ),
                audio_guider=MultiModalGuider(
                    params=audio_guider_params,
                    negative_context=a_context_n,
                ),
            ),
            sigmas=sigmas,
            noiser=noiser,
            stepper=stepper,
            width=stage_1_output_shape.width,
            height=stage_1_output_shape.height,
            frames=num_frames,
            fps=frame_rate,
            video=ModalitySpec(context=v_context_p, conditionings=stage_1_conditionings),
            audio=ModalitySpec(context=a_context_p),
            loop=res2s_audio_video_denoising_loop,
            streaming_prefetch_count=streaming_prefetch_count,
            max_batch_size=max_batch_size,
        )

        # Stage 2: Upsample and refine the video at higher resolution with distilled LoRA.
        upscaled_video_latent = self.upsampler(video_state.latent[:1])

        stage_2_sigmas = stage_2_sigmas.to(dtype=torch.float32, device=self.device)
        stage_2_output_shape = VideoPixelShape(batch=1, frames=num_frames, width=width, height=height, fps=frame_rate)
        stage_2_conditionings = self.image_conditioner(
            lambda enc: combined_image_conditionings(
                images=images,
                height=stage_2_output_shape.height,
                width=stage_2_output_shape.width,
                video_encoder=enc,
                dtype=dtype,
                device=self.device,
            )
        )

        video_state, audio_state = self.stage_2(
            denoiser=SimpleDenoiser(v_context=v_context_p, a_context=a_context_p),
            sigmas=stage_2_sigmas,
            noiser=noiser,
            stepper=stepper,
            width=width,
            height=height,
            frames=num_frames,
            fps=frame_rate,
            video=ModalitySpec(
                context=v_context_p,
                conditionings=stage_2_conditionings,
                noise_scale=stage_2_sigmas[0].item(),
                initial_latent=upscaled_video_latent,
            ),
            audio=ModalitySpec(
                context=a_context_p,
                noise_scale=stage_2_sigmas[0].item(),
                initial_latent=audio_state.latent,
            ),
            loop=res2s_audio_video_denoising_loop,
            streaming_prefetch_count=streaming_prefetch_count,
        )

        decoded_video = self.video_decoder(video_state.latent, tiling_config, generator)
        decoded_audio = self.audio_decoder(audio_state.latent)
        return decoded_video, decoded_audio


@torch.inference_mode()
def main() -> None:
    logging.getLogger().setLevel(logging.INFO)
    parser = hq_2_stage_arg_parser(params=LTX_2_3_HQ_PARAMS)
    args = parser.parse_args()
    pipeline = TI2VidTwoStagesHQPipeline(
        checkpoint_path=args.checkpoint_path,
        distilled_lora=args.distilled_lora,
        distilled_lora_strength_stage_1=args.distilled_lora_strength_stage_1,
        distilled_lora_strength_stage_2=args.distilled_lora_strength_stage_2,
        spatial_upsampler_path=args.spatial_upsampler_path,
        gemma_root=args.gemma_root,
        loras=tuple(args.lora) if args.lora else (),
        quantization=args.quantization,
        torch_compile=args.compile,
    )
    tiling_config = TilingConfig.default()
    video_chunks_number = get_video_chunks_number(args.num_frames, tiling_config)
    video, audio = pipeline(
        prompt=args.prompt,
        negative_prompt=args.negative_prompt,
        seed=args.seed,
        height=args.height,
        width=args.width,
        num_frames=args.num_frames,
        frame_rate=args.frame_rate,
        num_inference_steps=args.num_inference_steps,
        video_guider_params=MultiModalGuiderParams(
            cfg_scale=args.video_cfg_guidance_scale,
            stg_scale=args.video_stg_guidance_scale,
            rescale_scale=args.video_rescale_scale,
            modality_scale=args.a2v_guidance_scale,
            skip_step=args.video_skip_step,
            stg_blocks=args.video_stg_blocks,
        ),
        audio_guider_params=MultiModalGuiderParams(
            cfg_scale=args.audio_cfg_guidance_scale,
            stg_scale=args.audio_stg_guidance_scale,
            rescale_scale=args.audio_rescale_scale,
            modality_scale=args.v2a_guidance_scale,
            skip_step=args.audio_skip_step,
            stg_blocks=args.audio_stg_blocks,
        ),
        images=args.images,
        tiling_config=tiling_config,
        streaming_prefetch_count=args.streaming_prefetch_count,
        max_batch_size=args.max_batch_size,
    )

    encode_video(
        video=video,
        fps=args.frame_rate,
        audio=audio,
        output_path=args.output_path,
        video_chunks_number=video_chunks_number,
    )


if __name__ == "__main__":
    main()