github-actions[bot]
148 lines
5.4 KiB
Python
148 lines
5.4 KiB
Python
import logging
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from dataclasses import dataclass, field, replace
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import torch
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from safetensors import safe_open
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from ltx_core.components.guiders import MultiModalGuiderParams
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from ltx_core.types import SpatioTemporalScaleFactors
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# =============================================================================
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# Diffusion Schedule
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# =============================================================================
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# Noise schedule for the distilled pipeline. These sigma values control noise
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# levels at each denoising step and were tuned to match the distillation process.
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DISTILLED_SIGMA_VALUES = [1.0, 0.99375, 0.9875, 0.98125, 0.975, 0.909375, 0.725, 0.421875, 0.0]
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# Reduced schedule for super-resolution stage 2 (subset of distilled values)
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STAGE_2_DISTILLED_SIGMA_VALUES = [0.909375, 0.725, 0.421875, 0.0]
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DISTILLED_SIGMAS = torch.tensor(DISTILLED_SIGMA_VALUES)
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STAGE_2_DISTILLED_SIGMAS = torch.tensor(STAGE_2_DISTILLED_SIGMA_VALUES)
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# =============================================================================
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# Pipeline Parameters
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# =============================================================================
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@dataclass(frozen=True)
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class PipelineParams:
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seed: int = 10
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stage_1_height: int = 512
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stage_1_width: int = 768
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num_frames: int = 121
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frame_rate: float = 24.0
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num_inference_steps: int = 40
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video_guider_params: MultiModalGuiderParams = field(
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default_factory=lambda: MultiModalGuiderParams(
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cfg_scale=3.0,
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stg_scale=1.0,
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rescale_scale=0.7,
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modality_scale=3.0,
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skip_step=0,
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stg_blocks=[29],
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)
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)
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audio_guider_params: MultiModalGuiderParams = field(
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default_factory=lambda: MultiModalGuiderParams(
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cfg_scale=7.0,
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stg_scale=1.0,
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rescale_scale=0.7,
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modality_scale=3.0,
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skip_step=0,
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stg_blocks=[29],
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)
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)
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@property
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def stage_2_height(self) -> int:
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return int(self.stage_1_height * 2)
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@property
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def stage_2_width(self) -> int:
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return int(self.stage_1_width * 2)
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# Default params for LTX-2.0 non-distilled models. These can be overridden by detecting from checkpoint metadata.
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LTX_2_PARAMS = PipelineParams()
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# Default params for LTX-2.3 non-distilled models. These override some of the LTX-2.0 defaults.
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LTX_2_3_PARAMS = replace(
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LTX_2_PARAMS,
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num_inference_steps=30,
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video_guider_params=replace(LTX_2_PARAMS.video_guider_params, stg_blocks=[28]),
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audio_guider_params=replace(LTX_2_PARAMS.audio_guider_params, stg_blocks=[28]),
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)
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LTX_2_3_HQ_PARAMS = PipelineParams(
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num_inference_steps=15,
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stage_1_height=1088 // 2,
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stage_1_width=1920 // 2,
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video_guider_params=MultiModalGuiderParams(
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cfg_scale=3.0,
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stg_scale=0.0,
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rescale_scale=0.45,
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modality_scale=3.0,
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skip_step=0,
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stg_blocks=[],
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),
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audio_guider_params=MultiModalGuiderParams(
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cfg_scale=7.0,
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stg_scale=0.0,
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rescale_scale=1.0,
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modality_scale=3.0,
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skip_step=0,
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stg_blocks=[],
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),
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)
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DEFAULT_LORA_STRENGTH = 1.0
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DEFAULT_IMAGE_CRF = 33
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VIDEO_SCALE_FACTORS = SpatioTemporalScaleFactors.default()
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VIDEO_LATENT_CHANNELS = 128
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_LTX_2_3_MODEL_VERSION_PREFIX = "2.3"
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def detect_params(checkpoint_path: str) -> PipelineParams:
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"""Detect pipeline params from checkpoint metadata.
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Reads the ``model_version`` field from the safetensors config metadata.
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Returns ``LTX_2_3_PARAMS`` when the version starts with "2.3",
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otherwise falls back to ``LTX_2_PARAMS``.
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"""
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logger = logging.getLogger(__name__)
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try:
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with safe_open(checkpoint_path, framework="pt") as f:
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metadata = f.metadata() or {}
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version = metadata.get("model_version", "")
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except Exception:
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logger.warning("Could not read checkpoint metadata from %s, using LTX-2 defaults", checkpoint_path)
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return LTX_2_PARAMS
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if version.startswith(_LTX_2_3_MODEL_VERSION_PREFIX):
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return LTX_2_3_PARAMS
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logger.info("Using LTX_2_PARAMS for checkpoint (version=%s)", version or "unknown")
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return LTX_2_PARAMS
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# =============================================================================
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# Prompts
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# =============================================================================
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DEFAULT_NEGATIVE_PROMPT = (
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"blurry, out of focus, overexposed, underexposed, low contrast, washed out colors, excessive noise, "
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"grainy texture, poor lighting, flickering, motion blur, distorted proportions, unnatural skin tones, "
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"deformed facial features, asymmetrical face, missing facial features, extra limbs, disfigured hands, "
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"wrong hand count, artifacts around text, inconsistent perspective, camera shake, incorrect depth of "
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"field, background too sharp, background clutter, distracting reflections, harsh shadows, inconsistent "
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"lighting direction, color banding, cartoonish rendering, 3D CGI look, unrealistic materials, uncanny "
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"valley effect, incorrect ethnicity, wrong gender, exaggerated expressions, wrong gaze direction, "
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"mismatched lip sync, silent or muted audio, distorted voice, robotic voice, echo, background noise, "
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"off-sync audio, incorrect dialogue, added dialogue, repetitive speech, jittery movement, awkward "
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"pauses, incorrect timing, unnatural transitions, inconsistent framing, tilted camera, flat lighting, "
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"inconsistent tone, cinematic oversaturation, stylized filters, or AI artifacts."
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)
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