Davids048andClaude Fable 5.1
a5aa64ab6b
VideoGenerator.from_pretrained(model_path, config) takes the same nested
settings as from_config; the 24 flat keywords, from_pretrained_kwargs_to_config,
FROM_PRETRAINED_KWARGS, and every flat_field in the schema are deleted.
nvfp4_fa4 is the typed field engine.attention.nvfp4_fa4, applied by a
resolution step. 65 call sites and the docs use the nested form; the three
kwargs golden cases are config cases with byte-identical results.
Every value is decided during resolution; no runtime code calls
with_override. The device offload policy (unified memory, MPS, layerwise
conflicts, lazy module load) runs as resolution steps in the main process
(fastvideo/api/device_policy.py), and the worker runs on the config it
receives. The LTX-2 refine defaults from model_index.json and the MiniMax-H3
schedule from fastvideo_inference.json are resolution steps
(fastvideo/api/checkpoint_defaults.py) that read local checkpoints or download
only those files; the H3 pipeline validates the schedule against the loaded
schedulers but no longer writes it. The preprocessing entry point passes the
downloaded local path as run state instead of a config override. Teacher and
critic models load with override_transformer_cls_name as a load argument.
The test isolation turns the device policy off, as it blocks downloads, so
that the goldens do not depend on the machine.
Against the 3f6893a0 goldens, every field still matches except boundary_ratio
and ltx2_vae_tiling (DESIGN section 9) and the fields that no longer exist.
The launcher and trainer YAML verifications report 0 unexplained differences.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
107 lines
4.2 KiB
Python
107 lines
4.2 KiB
Python
"""Generate one LTX2 video and score it with VBench metrics.
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The generation block is the same as
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``examples/inference/basic/basic_ltx2.py`` — same prompt, same model,
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same shape, same num_frames. After ``shutdown()`` the script loads the
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mp4 back, builds a single :class:`fastvideo.eval.Evaluator`, and runs
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the prompt-aware VBench subset that's meaningful for an arbitrary
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text→video sample.
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The first run downloads CLIP / DINO / RAFT / AMT / ViCLIP / MUSIQ
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weights to ``~/.cache/fastvideo/eval/`` (~few GB total).
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GPU memory caveat
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-----------------
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Scoring 1088×1920×121 with all 8 metrics needs a dedicated GPU (~80 GB).
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On a shared GPU, ``vbench.motion_smoothness`` (AMT correlation volume)
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will OOM — its memory autoscale reads ``total_memory`` rather than
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``mem_get_info()`` free memory and therefore underestimates the
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required scale-down. Drop ``motion_smoothness`` from ``METRICS`` if
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sharing, or run on a smaller-resolution generation.
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"""
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import torch
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from fastvideo import VideoGenerator
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from fastvideo.eval import Evaluator
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from fastvideo.eval.io import build_eval_kwargs
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PROMPT = ("A warm sunny backyard. The camera starts in a tight cinematic close-up "
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"of a woman and a man in their 30s, facing each other with serious "
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"expressions. The woman, emotional and dramatic, says softly, \"That's "
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"it... Dad's lost it. And we've lost Dad.\" The man exhales, slightly "
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"annoyed: \"Stop being so dramatic, Jess.\" A beat. He glances aside, "
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"then mutters defensively, \"He's just having fun.\" The camera slowly "
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"pans right, revealing the grandfather in the garden wearing enormous "
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"butterfly wings, waving his arms in the air like he's trying to take "
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"off. He shouts, \"Wheeeew!\" as he flaps his wings with full commitment. "
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"The woman covers her face, on the verge of tears. The tone is deadpan, "
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"absurd, and quietly tragic.")
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# VBench sub-metrics meaningful for an arbitrary text→video sample
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# (just the generated frames, optionally fps + the source prompt).
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# Structured-prompt metrics (vbench.color, vbench.multiple_objects,
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# vbench.scene, ...) are excluded — they need prompts built to a
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# specific schema.
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METRICS = [
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"vbench.aesthetic_quality", # CLIP + LAION aesthetic head
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"vbench.subject_consistency", # DINO frame-to-first cosine
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"vbench.background_consistency", # DINO on background patches
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"vbench.imaging_quality", # pyiqa MUSIQ
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"vbench.temporal_flickering", # pixel-wise frame deltas
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"vbench.motion_smoothness", # AMT frame interpolator residual
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"vbench.dynamic_degree", # RAFT optical-flow magnitude (needs fps)
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"vbench.overall_consistency", # ViCLIP video↔prompt similarity
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]
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def main() -> None:
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# ----- generation (matches examples/inference/basic/basic_ltx2.py) -----
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generator = VideoGenerator.from_pretrained(
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"Davids048/LTX2-Base-Diffusers",
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{
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"engine": {
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"num_gpus": 1,
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},
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},
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)
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output_path = "outputs_video/ltx2_basic/output_ltx2_base_t2v_1088_1920_1.1.mp4"
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generator.generate({
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"prompt": PROMPT,
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"sampling": {
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"num_frames": 121,
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"height": 1088,
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"width": 1920,
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},
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"output": {
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"output_path": output_path,
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"save_video": True,
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},
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})
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generator.shutdown()
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# Free residual CUDA memory the generator left behind so the
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# evaluator can grab the largest possible workspace for AMT/RAFT.
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torch.cuda.empty_cache()
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# ----- scoring -----
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print(f"\n[eval] building evaluator: {METRICS}")
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evaluator = Evaluator(metrics=METRICS)
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# LTX2 outputs at 24 fps by default.
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sample = build_eval_kwargs({"prompt": PROMPT}, output_path, fps=24.0)
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print(f"[eval] running ({sample['video'].shape[1]} frames @ 24 fps)...")
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results = evaluator.evaluate(**sample)
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print("\n=== VBench scores ===")
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for name in METRICS:
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r = results[name]
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if r.score is None:
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reason = r.details.get("skipped", "no score")
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print(f" {name}: SKIPPED ({reason})")
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else:
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print(f" {name}: {r.score:.4f}")
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if __name__ == "__main__":
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main()
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