Delete app(draft).py

app(draft).py

@@ -1,373 +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")
-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()