51 lines
2.2 KiB
Python
51 lines
2.2 KiB
Python
"""Compare direct/upstream VAE tiled decode for one temporal latent clip."""
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import argparse
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import sys
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from pathlib import Path
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import torch
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from safetensors.torch import load_file
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from h3_blackwell_runtime.vae_decoder import MiniMaxH3VideoVAE as DirectVAE
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parser = argparse.ArgumentParser()
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parser.add_argument("--latent", type=Path, required=True)
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parser.add_argument("--start", type=int, default=0)
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parser.add_argument("--tokens", type=int, default=8)
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parser.add_argument("--comfy-path", default="/opt/ComfyUI")
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args = parser.parse_args()
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sys.path.insert(0, args.comfy_path)
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from h3_blackwell_runtime.upstream_vae import MiniMaxH3VideoVAE as UpstreamVAE # noqa: E402
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def stats(name: str, a: torch.Tensor, b: torch.Tensor) -> None:
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diff = (a.float() - b.float()).abs()
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frame_rows = []
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for index in range(diff.shape[2]):
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frame = diff[:, :, index]
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frame_rows.append((index, float(frame.max()), float(frame.mean()), float(torch.quantile(frame.flatten(), 0.99))))
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print({
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"stage": name,
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"shape": tuple(a.shape),
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"max": float(diff.max()),
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"mean": float(diff.mean()),
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"top_mean": sorted(frame_rows, key=lambda row: row[2], reverse=True)[:8],
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}, flush=True)
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state = torch.load(args.latent, map_location="cuda", weights_only=False)
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latent = state["latent"].to("cuda") if isinstance(state, dict) else state.to("cuda")
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direct = DirectVAE.from_safetensors("/vae/minimax_h3_video_vae_fp16.safetensors", device="cuda", tiling=True).eval()
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upstream = UpstreamVAE(tiling=True).to("cuda").eval()
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upstream.load_state_dict(load_file("/vae/minimax_h3_video_vae_fp16.safetensors", device="cuda"), strict=True)
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with torch.inference_mode():
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z_d = latent[:, :, args.start:args.start + args.tokens].to(next(direct.parameters()).dtype)
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z_u = latent[:, :, args.start:args.start + args.tokens].to(next(upstream.parameters()).dtype)
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z_d = z_d * direct.latents_std.view(1, -1, 1, 1, 1).to(z_d) + direct.latents_mean.view(1, -1, 1, 1, 1).to(z_d)
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z_u = z_u * upstream.latents_std.view(1, -1, 1, 1, 1).to(z_u) + upstream.latents_mean.view(1, -1, 1, 1, 1).to(z_u)
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stats("tiled_clip", direct.tiled_decode(z_d), upstream.tiled_decode(z_u))
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