191 lines
7.1 KiB
Python
191 lines
7.1 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import json
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import os
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from pathlib import Path
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import sys
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import pytest
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import torch
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from safetensors import safe_open
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from safetensors.torch import load_file
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from torch.testing import assert_close
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repo_root = Path(__file__).resolve().parents[3]
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ltx_core_path = repo_root / "LTX-2" / "packages" / "ltx-core" / "src"
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if ltx_core_path.exists() and str(ltx_core_path) not in sys.path:
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sys.path.insert(0, str(ltx_core_path))
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from fastvideo.models.vaes.ltx2vae import LTX2VideoDecoder, LTX2VideoEncoder
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def _load_metadata(path: Path) -> dict:
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with safe_open(str(path), framework="pt") as f:
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meta = f.metadata()
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if not meta or "config" not in meta:
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raise KeyError("Missing config metadata in safetensors file.")
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return json.loads(meta["config"])
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def _load_weights(path: Path) -> dict[str, torch.Tensor]:
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print(f"[LTX2 VAE TEST] Loading weights from {path}")
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return load_file(str(path))
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def _select_vae_weights(weights: dict[str, torch.Tensor], prefix: str) -> dict[str, torch.Tensor]:
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filtered: dict[str, torch.Tensor] = {}
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alt_prefix = prefix.replace("vae.", "")
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for name, tensor in weights.items():
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if name.startswith(prefix):
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filtered[name.replace(prefix, "")] = tensor
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elif alt_prefix and name.startswith(alt_prefix):
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filtered[name.replace(alt_prefix, "")] = tensor
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elif name.startswith("vae.per_channel_statistics."):
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filtered[name.replace("vae.", "")] = tensor
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elif name.startswith("per_channel_statistics."):
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filtered[name] = tensor
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print(f"[LTX2 VAE TEST] Selected {len(filtered)} tensors for {prefix}")
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return filtered
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def _load_into_model(model: torch.nn.Module, weights: dict[str, torch.Tensor]) -> tuple[int, list[str]]:
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model_state = model.state_dict()
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filtered = {
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k: v
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for k, v in weights.items()
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if k in model_state and model_state[k].shape == v.shape
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}
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missing = [k for k in model_state.keys() if k not in filtered]
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print(
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f"[LTX2 VAE TEST] Loading {len(filtered)} / {len(model_state)} tensors "
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f"from {len(weights)} available"
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)
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if not filtered:
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return 0, missing
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model.load_state_dict(filtered, strict=False)
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return len(filtered), missing
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def test_ltx2_vae_parity():
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diffusers_root = Path(
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os.getenv("LTX2_DIFFUSERS_PATH", "converted/ltx2_diffusers")
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)
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official_path = Path(
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os.getenv(
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"LTX2_OFFICIAL_PATH",
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"official_ltx_weights/ltx-2-19b-distilled.safetensors",
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)
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)
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fastvideo_path = Path(
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os.getenv("LTX2_VAE_PATH", str(diffusers_root / "vae"))
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)
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if not official_path.exists():
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pytest.skip(f"LTX-2 weights not found at {official_path}")
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if not fastvideo_path.exists():
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pytest.skip(f"LTX-2 diffusers VAE not found at {fastvideo_path}")
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config = _load_metadata(official_path)
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if "vae" not in config:
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pytest.skip("VAE config not found in safetensors metadata.")
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try:
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from ltx_core.model.video_vae import VideoDecoderConfigurator, VideoEncoderConfigurator
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except ImportError as exc:
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pytest.skip(f"LTX-2 import failed: {exc}")
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ref_weights = _load_weights(official_path)
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encoder_weights = _select_vae_weights(ref_weights, "vae.encoder.")
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decoder_weights = _select_vae_weights(ref_weights, "vae.decoder.")
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if not encoder_weights or not decoder_weights:
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pytest.skip("VAE weights not found in safetensors file.")
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fastvideo_weights_path = fastvideo_path / "model.safetensors"
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if not fastvideo_weights_path.exists():
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pytest.skip(f"FastVideo VAE weights not found at {fastvideo_weights_path}")
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fastvideo_weights = _load_weights(fastvideo_weights_path)
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fastvideo_encoder_weights = _select_vae_weights(
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fastvideo_weights, "encoder."
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)
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fastvideo_decoder_weights = _select_vae_weights(
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fastvideo_weights, "decoder."
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)
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if not fastvideo_encoder_weights or not fastvideo_decoder_weights:
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pytest.skip("FastVideo VAE weights not found in diffusers file.")
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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precision = torch.bfloat16 if torch.cuda.is_available() else torch.float32
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fastvideo_encoder = LTX2VideoEncoder(config).to(device=device, dtype=precision)
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fastvideo_decoder = LTX2VideoDecoder(config).to(device=device, dtype=precision)
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ref_encoder = VideoEncoderConfigurator.from_config(config).to(device=device, dtype=precision)
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ref_decoder = VideoDecoderConfigurator.from_config(config).to(device=device, dtype=precision)
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loaded_fastvideo_encoder, missing_fastvideo_encoder = _load_into_model(
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fastvideo_encoder.model, fastvideo_encoder_weights
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)
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loaded_ref_encoder, missing_ref_encoder = _load_into_model(ref_encoder, encoder_weights)
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loaded_fastvideo_decoder, missing_fastvideo_decoder = _load_into_model(
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fastvideo_decoder.model, fastvideo_decoder_weights
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)
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loaded_ref_decoder, missing_ref_decoder = _load_into_model(ref_decoder, decoder_weights)
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if min(
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loaded_fastvideo_encoder,
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loaded_ref_encoder,
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loaded_fastvideo_decoder,
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loaded_ref_decoder,
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) == 0:
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pytest.skip("Failed to load VAE weights into one or more models.")
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if (
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missing_fastvideo_encoder
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or missing_ref_encoder
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or missing_fastvideo_decoder
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or missing_ref_decoder
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):
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print(f"[LTX2 VAE TEST] Missing encoder keys: {len(missing_fastvideo_encoder)}")
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print(f"[LTX2 VAE TEST] Missing decoder keys: {len(missing_fastvideo_decoder)}")
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pytest.skip("Missing VAE keys; cannot ensure parity.")
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fastvideo_encoder.model.eval()
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fastvideo_decoder.model.eval()
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ref_encoder.eval()
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ref_decoder.eval()
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fastvideo_decoder.model.decode_noise_scale = 0.0
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ref_decoder.decode_noise_scale = 0.0
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batch_size = 1
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frames = 9
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height = 64
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width = 64
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video = torch.randn(
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batch_size,
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3,
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frames,
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height,
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width,
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device=device,
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dtype=precision,
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)
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with torch.no_grad():
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ref_latents = ref_encoder(video)
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fast_latents = fastvideo_encoder(video)
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assert ref_latents.shape == fast_latents.shape
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assert ref_latents.dtype == fast_latents.dtype
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assert torch.isfinite(ref_latents).all(), "Reference encoder produced non-finite latents."
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assert torch.isfinite(fast_latents).all(), "FastVideo encoder produced non-finite latents."
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assert_close(ref_latents, fast_latents, atol=1e-2, rtol=1e-2)
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timestep = torch.tensor([0.05], device=device, dtype=precision)
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with torch.no_grad():
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ref_decoded = ref_decoder(ref_latents, timestep=timestep)
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fast_decoded = fastvideo_decoder(fast_latents, timestep=timestep)
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assert ref_decoded.shape == fast_decoded.shape
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assert ref_decoded.dtype == fast_decoded.dtype
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assert torch.isfinite(ref_decoded).all(), "Reference decoder produced non-finite output."
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assert torch.isfinite(fast_decoded).all(), "FastVideo decoder produced non-finite output."
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assert_close(ref_decoded, fast_decoded, atol=1e-2, rtol=1e-2)
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