1311 lines
60 KiB
Python
1311 lines
60 KiB
Python
"""H3 Motion Context clip stitcher for ComfyUI.
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Loads NikoDemon80/ComfyUI-H3-Motion-Context clip archive files (h3_motion_context_av_v1),
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decodes each approved clip once, crossfades the carried Motion Context head from
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clips, and concatenates the picture/audio into one IMAGE + AUDIO pair.
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This intentionally does NOT reconstruct a NestedTensor and feed the saved files back
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into Motion Context.
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The archive format is the sampler output, and this node is a final-media assembly tool.
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"""
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import fnmatch
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import gc
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import glob
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import inspect
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import logging
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import os
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import re
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import torch
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import comfy.model_management
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import torch.nn.functional as F
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import folder_paths
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from comfy.utils import ProgressBar
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try:
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from comfy_execution.graph_utils import get_original_node_id
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except Exception:
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get_original_node_id = None
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try:
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from safetensors.torch import load_file as st_load
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except Exception:
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st_load = None
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try:
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import torchaudio
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except Exception:
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torchaudio = None
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log_ = logging.getLogger("h3_motion_context_clip_stitcher")
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def _resolve_folder(path):
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p = (path or "").strip().strip('"').strip("'")
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if not p:
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p = "h3_context"
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candidates = [p, os.path.join(folder_paths.get_output_directory(), p)]
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for c in candidates:
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if os.path.isdir(c):
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return os.path.abspath(c)
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raise FileNotFoundError("H3 Motion Context Clip Stitcher: folder not found: %s\n"
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"You can use an absolute path or a path relative to "
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"ComfyUI's output folder." % p)
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def _clip_number(path):
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name = os.path.basename(path)
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# noinspection RegExpUnnecessaryNonCapturingGroup
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pat = re.compile(r"(?:^|_)(\d{5})(?:\.safetensors)$", re.IGNORECASE)
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m = pat.search(name)
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return int(m.group(1)) if m else -1
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def _find_files(folder, pattern, first_clip, last_clip):
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pattern = (pattern or "clip_*.safetensors").strip()
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paths = []
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for p in glob.glob(os.path.join(folder, pattern)):
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if not os.path.isfile(p):
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continue
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if not p.lower().endswith(".safetensors"):
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continue
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idx = _clip_number(p)
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if idx < 0:
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continue
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if idx < int(first_clip):
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continue
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if 0 < int(last_clip) < idx:
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continue
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paths.append((idx, p))
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paths.sort(key=lambda x: x[0])
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if not paths:
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raise FileNotFoundError("H3 Motion Context Clip Stitcher: no numbered "
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".safetensors files matched '%s' in %s."
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% (pattern, folder))
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# Do not silently skip a missing numbered clip. A gap usually means an
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# approved clip was not saved, and silently stitching around it would make
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# a misleading final timeline.
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expected = paths[0][0]
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for idx, _ in paths:
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if idx != expected:
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raise ValueError("H3 Motion Context Clip Stitcher: missing clip %05d between "
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"the selected archive files." % expected)
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expected += 1
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return paths
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def _load_archive(path):
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if st_load is None:
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raise RuntimeError("safetensors is unavailable in this "
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"ComfyUI Python environment.")
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# noinspection PyCallingNonCallable
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data = st_load(path, device="cpu")
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if "video" not in data or "audio" not in data:
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raise ValueError("%s is not an h3_motion_context_av_v1 archive: "
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"expected 'video' and 'audio'." % path)
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video = data["video"]
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audio = data["audio"]
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if video.ndim != 5:
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raise ValueError("%s: expected video [B,C,T,H,W], got %s"
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% (path, tuple(video.shape)))
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if audio.ndim != 4:
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raise ValueError("%s: expected audio [B,C,2,T], got %s"
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% (path, tuple(audio.shape)))
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if video.shape[0] != 1 or audio.shape[0] != 1:
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raise ValueError("%s: only batch size 1 archive clips are supported." % path)
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return video, audio
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def _decode_video(vae, video_latent):
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""" Decode the H3 video stream and normalize to ComfyUI IMAGE format.
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Large latents (1MP and up) go through an explicit tiled decode with
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conservative tile sizes instead of vae.decode()'s adaptive heuristics,
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which can pick tiles that are far too aggressive and abort the native
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VAE kernels. decode_tiled is introspected so only supported kwargs are
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passed; small latents just use the plain decode path.
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"""
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decode_fn = vae.decode
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kwargs = {}
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# video_latent is [B,C,T,H,W] or [C,T,H,W]; the spatial size is the last
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# two dims in either case.
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spatial = tuple(int(v) for v in video_latent.shape[-2:])
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latent_hw = spatial[0] * spatial[1]
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# The latent is ~8x downscaled from the pixel resolution, so ~1MP of
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# video corresponds to a latent spatial product around 16k pixels.
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tiled = getattr(vae, "decode_tiled", None)
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if callable(tiled) and latent_hw is not None and latent_hw >= 128 * 96:
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try:
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sig = inspect.signature(tiled)
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supported = set(sig.parameters)
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except (TypeError, ValueError):
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supported = set()
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if supported:
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# Mirror comfy-core's VAEDecodeTiled defaults, then scale the
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# spatial tiles down for very large latents so per-tile memory
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# stays bounded on 1MP+ latents.
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kwargs = {"tile_x": 256, "tile_y": 256, "tile_t": 64, "overlap": 64}
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if latent_hw >= 128 * 96: # ~1MP+ of video space: shrink tiles
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kwargs.update(tile_x=192, tile_y=192)
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kwargs = {k: v for k, v in kwargs.items() if k in supported}
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decode_fn = tiled
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log_.info("H3 clip stitcher: using decode_tiled %s for %dx%d latent.",
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kwargs, spatial[0], spatial[1])
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try:
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images = decode_fn(video_latent, **kwargs)
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except TypeError: # Signature mismatch fallback: plain decode.
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images = vae.decode(video_latent)
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# H3's VAE normally returns [B,T,H,W,C]. Some VAE implementations can
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# return [T,H,W,C], so accept both.
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if images.ndim == 5:
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images = images.reshape(-1, *images.shape[-3:])
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elif images.ndim != 4:
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raise RuntimeError("H3 video VAE returned unexpected shape %s"
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% (tuple(images.shape),))
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return images.to(torch.float32).clamp(0, 1).cpu()
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def _decode_audio(audio_vae, audio_latent):
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""" Decode the H3 audio stream using the same convention as ComfyUI's VAEDecodeAudio.
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"""
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audio = audio_vae.decode(audio_latent)
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# Current ComfyUI audio VAE returns [B,L,C]. Convert to [B,C,L].
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if audio.ndim != 3:
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raise RuntimeError("H3 audio VAE returned unexpected shape %s" % (tuple(audio.shape),))
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audio = audio.movedim(-1, 1)
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sr = int(getattr(audio_vae, "audio_sample_rate_output",
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getattr(audio_vae, "audio_sample_rate", 32000)))
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return {"waveform": audio.to(torch.float32).cpu(), "sample_rate": sr}
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def _resample_audio(audio, target_sr):
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if audio is None:
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return None
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sr = int(audio["sample_rate"])
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if sr == int(target_sr):
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return audio
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if torchaudio is None:
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raise RuntimeError("Audio sample rates differ (%d vs %d), but torchaudio is "
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"unavailable to resample them." % (sr, int(target_sr)))
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# noinspection PyUnresolvedReferences
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waveform = torchaudio.functional.resample(audio["waveform"], sr, int(target_sr))
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return {"waveform": waveform, "sample_rate": int(target_sr)}
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def _crossfade_boundary(prev_tail_images, cur_images, prev_tail_wave, cur_wave,
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overlap_frames, cross_samples):
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""" Crossfade the previous clip's tail with the current clip's head.
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prev_tail_images: [L,H,W,C] cur_images: [T,H,W,C]
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prev_tail_wave : [1,C,Ls] cur_wave: [1,C,Cs] (or None)
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Returns (blend_images [L,H,W,C], blend_wave [1,C,Ls] or None).
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Video uses a linear dissolve ramp; audio uses an equal-power (cos/sin)
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ramp over the same time window so picture and sound stay in sync.
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"""
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L = int(overlap_frames)
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if L <= 0:
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return cur_images[:0], None
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if L == 1:
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alpha = torch.full((1, 1, 1, 1), 0.5, dtype=prev_tail_images.dtype,
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device=prev_tail_images.device)
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else:
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alpha = torch.linspace(0.0, 1.0, L, dtype=prev_tail_images.dtype,
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device=prev_tail_images.device).view(L, 1, 1, 1)
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blend_images = prev_tail_images * (1.0 - alpha) + cur_images[:L] * alpha
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blend_wave = None
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if prev_tail_wave is not None and cur_wave is not None:
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n = int(cross_samples)
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if n <= 0:
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blend_wave = prev_tail_wave
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else:
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n = min(n, int(prev_tail_wave.shape[-1]), int(cur_wave.shape[-1]))
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theta = torch.linspace(0.0, 1.5707963267948966, n, dtype=prev_tail_wave.dtype,
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device=prev_tail_wave.device).view(1, 1, n)
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blend_wave = (prev_tail_wave[..., :n] * torch.cos(theta)
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+ cur_wave[..., :n] * torch.sin(theta))
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return blend_images, blend_wave
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# --- Texture ratchet correction (ported from ComfyUI-Hand-Tie-Clips/latents.py) ---
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# The texture ratchet: high-band noise/grain increases monotonically at each join.
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# The statistic is band_ratio = high-band std / total std.
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# Measured across a chain it goes 0.3643 -> 0.3673 -> 0.3702 (monotone increase).
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# The fix: match_band rescales only the high-frequency band to a target ratio.
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def _band_gauss1d(sigma, device, dtype):
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r = max(1, int(round(3.0 * float(sigma))))
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x = torch.arange(-r, r + 1, dtype=torch.float32, device=device)
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k = torch.exp(-(x * x) / (2.0 * float(sigma) ** 2))
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return (k / k.sum()).to(dtype)
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def _band_split(t, sigma=2.0):
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"""Separable Gaussian low/high split over the last two dims. -> (lo, hi).
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Returns None when the tensor has no spatial extent to speak of.
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Replicate padding, not reflect: a latent's spatial dims are small and
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reflect needs the pad to be smaller than the dimension.
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"""
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if t.dim() < 2 or t.shape[-1] < 8 or t.shape[-2] < 8:
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return None
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# noinspection PyUnresolvedReferences
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import torch.nn.functional as f
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h, w = int(t.shape[-2]), int(t.shape[-1])
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flat = t.reshape(-1, 1, h, w)
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k = _band_gauss1d(sigma, t.device, t.dtype)
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pad = k.numel() // 2
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lo = f.conv2d(f.pad(flat, (pad, pad, 0, 0), mode="replicate"),
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k.view(1, 1, 1, -1))
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lo = f.conv2d(f.pad(lo, (0, 0, pad, pad), mode="replicate"),
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k.view(1, 1, -1, 1))
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lo = lo.reshape(t.shape)
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return lo, t - lo
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def _band_ratio(t, sigma=2.0):
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"""High-band sigma as a fraction of total sigma. -> float, or None.
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This is the statistic the texture ratchet actually moves.
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Measured across a chain it goes 0.3643 -> 0.3673 -> 0.3702 (monotone increase).
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"""
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got = _band_split(t, sigma)
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if got is None:
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return None
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lo, hi = got
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tot = float(t.float().std())
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if not tot or tot != tot:
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return None
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hi_sd = float(hi.float().std())
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if not hi_sd or hi_sd != hi_sd:
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return None
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return hi_sd / tot
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def _match_band(t, target_ratio, sigma=2.0, clamp=(0.5, 2.0)):
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"""Rescale t's high band so its high-band fraction becomes `target_ratio`.
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-> (tensor, k), or (t, None) when the tensor has no bands to match.
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Only the high half is scaled, so the low-frequency structure that carries
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the scene is bit-identical and the correction is one scalar. It cannot blur,
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sharpen unevenly, or invent detail; the worst it can do is get the gain
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wrong, which is why `clamp` exists.
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The fraction is against the tensor's own sigma, so restoring the ratio
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does move total sigma a little. That is deliberate: the ratio is the drifting
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statistic and sigma is the one that lies.
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"""
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got = _band_split(t, sigma)
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if got is None or not target_ratio:
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return t, None
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lo, hi = got
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cur_hi = float(hi.float().std())
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if not cur_hi or cur_hi != cur_hi:
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return t, None
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# The naive `k = target * sigma / hi_sigma` is wrong, and quietly so:
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# scaling the high band changes the sigma it is a fraction OF, so the
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# target moves as you apply it. Measured, it undershot by 5% on a
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# latent-shaped tensor -- a correction that silently does most, but not
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# all, of its job is the worst kind to ship.
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#
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# First guess solves the fixed point assuming lo and hi are orthogonal:
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# k*H / sqrt(L^2 + k^2*H^2) = r, so k = r*L / (H*sqrt(1 - r^2)) ...
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r = min(float(target_ratio), 0.999)
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lo_sd = float(_band_split(t, 2.0)[0].float().std()) if _band_split(t, 2.0) else 0.0
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if not lo_sd:
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return t, None
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k = (r * lo_sd) / (cur_hi * max(1e-6, (1.0 - r * r) ** 0.5))
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# ... then refine against the statistic as actually measured, because a
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# difference of Gaussians is not an exact orthogonal projection. Two or
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# three passes converge, and a latent is small enough that this is free.
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for _ in range(4):
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k = min(max(k, clamp[0]), clamp[1])
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got_r = _band_ratio(lo + hi * k, 2.0)
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if not got_r:
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break
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if abs(got_r - r) <= 1e-4 * max(r, 1e-6):
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break
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k *= r / got_r
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k = min(max(k, clamp[0]), clamp[1])
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return lo + hi * k, k
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def _av_from_live_latent(latent):
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""" Extract (video, audio) tensors from an in-memory H3 AV LATENT,
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using the same unpacking convention as NikoDemon80's own
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_streams_from_latent()/save(): latent["samples"] is a NestedTensor
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(or tuple/list) whose unbind() gives (video, audio) in that order.
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"""
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if not isinstance(latent, dict) or "samples" not in latent:
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raise ValueError("h3_motion_context: expected a MiniMax H3 AV latent dict with "
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"a 'samples' key, got %r" % type(latent))
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samples = latent["samples"]
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if hasattr(samples, "unbind"):
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parts = list(samples.unbind())
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elif isinstance(samples, (tuple, list)):
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parts = list(samples)
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else:
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raise ValueError("h3_motion_context: expected a MiniMax H3 AV latent (a nested "
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"video/audio pair), got %r" % type(samples))
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if len(parts) < 2:
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raise ValueError("h3_motion_context: latent has no audio stream; wire the "
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"sampler output of an H3 AV graph.")
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# NestedTensor.unbind() returns views into the packed underlying storage.
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# Passing such views (or tensors still carrying nested metadata) to a VAE's
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# CUDA kernels can trigger cudaErrorIllegalAddress. Force a real, dense,
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# detached CPU copy of each stream before handing them to the VAE.
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video = parts[0].detach().to("cpu", copy=True).contiguous()
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audio = parts[1].detach().to("cpu", copy=True).contiguous()
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# Live streams can carry the same shapes as the archive files (video
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# [B,C,T,H,W] or [C,T,H,W]; audio [B,C,2,T] or [B,L,C]).
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expected_ndim = {"video": (4, 5), "audio": (3, 4)}
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for name, t in (("video", video), ("audio", audio)):
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if t.ndim not in expected_ndim[name]:
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raise ValueError("h3_motion_context: live %s stream has unexpected "
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"shape %s." % (name, tuple(t.shape)))
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if not torch.is_floating_point(t):
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raise ValueError("h3_motion_context: live %s stream is not a float "
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"tensor (dtype %s)." % (name, t.dtype))
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return video, audio
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def _fmt_size(num_bytes):
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size = float(num_bytes)
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for unit in ("B", "KiB", "MiB", "GiB"):
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if size < 1024.0:
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return "%.1f %s" % (size, unit)
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size /= 1024.0
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return "%.1f TiB" % size
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class _AVStreamPair:
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"""Minimal stand-in for a NestedTensor: wraps (video, audio) tensors and
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exposes the unbind() interface that comfy-core's LTXVSeparateAVLatent
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(and the H3 sampler code) expects. The wrapped tensors are always dense,
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detached, contiguous copies, so they are safe to feed to the VAE kernels.
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"""
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def __init__(self, video, audio):
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self._parts = [video, audio]
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def unbind(self):
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# noinspection PyTypeChecker
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return tuple(self._parts)
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def __iter__(self):
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return iter(self._parts)
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def __len__(self):
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return len(self._parts)
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class H3MotionContextClipStitcher:
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""" Load, decode, and crossfade approved H3 Motion Context clips.
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {"required": {"folder": ("STRING", {"default": "h3_context",
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"tooltip": "Folder containing clip_00001.safetensors, "
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"clip_00002.safetensors, etc.\nAbsolute paths and paths relative "
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"to ComfyUI/output are accepted."}),
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"pattern": ("STRING", {"default": "clip_*.safetensors",
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"tooltip": "Filename glob. The final five-digit number is treated as "
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"the clip index."}),
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"first_clip": ("INT", {"default": 1, "min": 1, "max": 9999,
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"tooltip": "First approved clip to include."}),
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"last_clip": ("INT", {"default": 0, "min": 0, "max": 9999,
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"tooltip": "Last clip to include. 0 = every clip from first_clip onward."}),
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"context_length": (["5", "22", "39", "56"], {"default": "22",
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"tooltip": "Number of decoded frames to crossfade at each clip boundary. "
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"The normal setting is 22 frames.\n"
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"This is the overlap length that is dissolved between "
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"adjacent clips.\n"
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"5, 22, 39 or 56 are the lengths that are a whole number of "
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"latent steps, which is why other numbers aren't offered."}),
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"fps": ("FLOAT", {"default": 24.0, "min": 1.0, "max": 240.0, "step": 0.001,
|
|
"tooltip": "H3 native output rate. Keep this at 24 unless your workflow "
|
|
"deliberately changes it."}), },
|
|
"optional": {"video_vae": ("VAE", {"tooltip": "MiniMax H3 video VAE "
|
|
"(FP16 or INT8 ConvRot)."}),
|
|
"audio_vae": ("VAE", {
|
|
"tooltip": "MiniMax H3 audio VAE FP32. Required for the AUDIO "
|
|
"output."}),
|
|
"latent": ("LATENT", {
|
|
"tooltip": "Optional: the currently-generated AV latent (from your "
|
|
"H3 sampler), used in place of the highest-numbered file "
|
|
"on disk."}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE", "AUDIO", "INT", "STRING")
|
|
RETURN_NAMES = ("images", "audio", "frame_count", "report")
|
|
FUNCTION = "stitch"
|
|
CATEGORY = "noEmbryo/MiniMax H3"
|
|
DESCRIPTION = ("Final assembly for NikoDemon80's H3 Motion Context AV clip archives.\n"
|
|
"Loads numbered h3_motion_context_av_v1 files, decodes one clip at a "
|
|
"time, dissolves the overlap between adjacent clips (video + "
|
|
"synchronized audio), and concatenates them to a final video and audio stream.")
|
|
|
|
# noinspection PyUnusedLocal
|
|
@classmethod
|
|
def IS_CHANGED(cls, folder, pattern, first_clip, last_clip, context_length, fps,
|
|
video_vae=None, audio_vae=None, latent=None):
|
|
# noinspection PyBroadException
|
|
try:
|
|
d = _resolve_folder(folder)
|
|
files = _find_files(d, pattern, first_clip, last_clip)
|
|
# noinspection PyTypeChecker
|
|
return tuple((p, os.stat(p).st_mtime_ns, os.path.getsize(p))
|
|
for _, p in files) + (int(context_length), float(fps),)
|
|
except Exception:
|
|
return float("NaN")
|
|
|
|
@staticmethod
|
|
def stitch(folder, pattern, first_clip, last_clip, context_length, fps,
|
|
video_vae=None, audio_vae=None, latent=None,):
|
|
if video_vae is None:
|
|
raise ValueError("Connect your MiniMax H3 video VAE to 'video_vae'.")
|
|
if st_load is None:
|
|
raise RuntimeError("safetensors is not available in this ComfyUI environment")
|
|
|
|
d = _resolve_folder(folder)
|
|
files = _find_files(d, pattern, first_clip, last_clip)
|
|
live_entry = None
|
|
|
|
live_index = int(first_clip)
|
|
if latent is not None:
|
|
if files:
|
|
# The last on-disk file is presumed to be the live latent's own
|
|
# saved duplicate; drop it (with a warning) and renumber.
|
|
log_.warning("H3 clip stitcher: dropping on-disk clip %05d; it is "
|
|
"presumed to be the live latent's duplicate.",
|
|
files[-1][0])
|
|
files = files[:-1]
|
|
live_index = files[-1][0] + 1 if files else int(first_clip)
|
|
video_latent, audio_latent = _av_from_live_latent(latent)
|
|
live_entry = (live_index, None, video_latent, audio_latent) # path=None marks it as live
|
|
|
|
image_parts = []
|
|
audio_parts = []
|
|
report_lines = []
|
|
target_sr = None
|
|
|
|
overlap = int(context_length)
|
|
|
|
prev_tail_img = None
|
|
prev_tail_wave = None
|
|
|
|
all_entries = [(idx, path, None, None) for idx, path in files]
|
|
if live_entry is not None:
|
|
# noinspection PyTypeChecker
|
|
all_entries.append(live_entry)
|
|
|
|
total_count = len(files) + (1 if live_entry is not None else 0)
|
|
# noinspection PyCallingNonCallable
|
|
pbar = ProgressBar(total_count, node_id=get_original_node_id()
|
|
if get_original_node_id is not None else None)
|
|
log_.info("H3 clip stitcher: %d clip(s) selected from %s", total_count, d)
|
|
|
|
# Degenerate crossfade (no overlap or a single clip) falls back to a
|
|
# plain concatenation, which is exactly what the trim modes would do.
|
|
crossfade_active = overlap > 0 and len(all_entries) > 1
|
|
|
|
for pos, (idx, path, live_video, live_audio) in enumerate(all_entries):
|
|
if path is not None:
|
|
video_latent, audio_latent = _load_archive(path)
|
|
else:
|
|
video_latent, audio_latent = live_video, live_audio
|
|
log_.info("H3 clip stitcher: clip %05d taken from live latent input",
|
|
idx)
|
|
|
|
# Decode one clip at a time. The decoded result is immediately moved
|
|
# to CPU, so a long chain does not keep every VAE result on VRAM.
|
|
images = _decode_video(video_vae, video_latent)
|
|
del video_latent
|
|
|
|
audio = None
|
|
if audio_vae is not None:
|
|
audio = _decode_audio(audio_vae, audio_latent,
|
|
# normalize=normalize_audio_per_clip
|
|
)
|
|
del audio_latent
|
|
# Keep VRAM/RAM flat across clips: free the freshly cached decode
|
|
# blocks so a long chain (or a big 1MP clip) cannot accumulate.
|
|
gc.collect()
|
|
comfy.model_management.soft_empty_cache()
|
|
|
|
decoded_frames = int(images.shape[0])
|
|
is_last = pos == len(all_entries) - 1
|
|
|
|
if not crossfade_active:
|
|
# Single clip (or zero overlap): no boundaries to blend, just
|
|
# emit the whole decoded clip and finish.
|
|
if audio is not None and target_sr is None:
|
|
target_sr = int(audio["sample_rate"])
|
|
image_parts.append(images)
|
|
if audio is not None:
|
|
audio_parts.append(audio["waveform"])
|
|
report_lines.append("clip_%05d: decoded=%d frames, no crossfade "
|
|
"(single clip), audio=%.4fs"
|
|
% (idx, decoded_frames, 0.0
|
|
if audio is None else audio["waveform"].shape[-1]
|
|
/ float(audio["sample_rate"])))
|
|
pbar.update_absolute(pos + 1, total_count)
|
|
del images
|
|
if audio is not None:
|
|
del audio
|
|
continue
|
|
|
|
if crossfade_active:
|
|
if decoded_frames < 2 * overlap:
|
|
raise ValueError("Crossfade requires each clip to have at least "
|
|
"2*context_length (%d) frames; clip %05d has %d."
|
|
% (overlap, idx, decoded_frames))
|
|
|
|
# Resample this clip's audio to the shared target rate before
|
|
# splitting, so the head/tail sample counts line up across clips.
|
|
if audio is not None:
|
|
if target_sr is None:
|
|
target_sr = int(audio["sample_rate"])
|
|
audio = _resample_audio(audio, target_sr)
|
|
if prev_tail_wave is not None:
|
|
prev_tail_wave = _resample_audio(prev_tail_wave, target_sr)
|
|
|
|
n = 0
|
|
if audio is not None:
|
|
sr = int(audio["sample_rate"])
|
|
n = int(round((overlap / float(fps)) * sr))
|
|
if n <= 0:
|
|
n = 1
|
|
if n >= audio["waveform"].shape[-1]:
|
|
raise ValueError("Audio is too short to extract a %d-frame "
|
|
"(%0.4fs) crossfade head/tail for clip %05d."
|
|
% (overlap, overlap / float(fps), idx))
|
|
|
|
# # Clamp first clip for consistency (tone compensation is now handled
|
|
# # by H3ClipRefiner node placed inline between sampler and Save Latent)
|
|
# if pos == 0:
|
|
# images = images.clamp(0.0, 1.0)
|
|
|
|
head_img = images[:overlap]
|
|
body_img = images[overlap:-overlap]
|
|
tail_img = images[-overlap:]
|
|
|
|
head_wave = body_wave = tail_wave = None
|
|
if audio is not None:
|
|
wave = audio["waveform"]
|
|
sr = int(audio["sample_rate"])
|
|
head_wave = {"waveform": wave[..., :n], "sample_rate": sr}
|
|
body_wave = {"waveform": wave[..., n:-n], "sample_rate": sr}
|
|
tail_wave = {"waveform": wave[..., -n:], "sample_rate": sr}
|
|
|
|
if pos == 0:
|
|
# First clip: emit head+body raw, buffer the tail for the next boundary.
|
|
image_parts.append(torch.cat([head_img, body_img], dim=0))
|
|
if audio is not None:
|
|
audio_parts.append(torch.cat([head_wave["waveform"],
|
|
body_wave["waveform"]], dim=-1))
|
|
prev_tail_img = tail_img
|
|
prev_tail_wave = tail_wave
|
|
else:
|
|
blend_img, blend_wave = _crossfade_boundary(prev_tail_img, images,
|
|
prev_tail_wave[
|
|
"waveform"] if prev_tail_wave is not None else None,
|
|
audio["waveform"] if audio is not None else None, overlap, n)
|
|
image_parts.append(blend_img)
|
|
if audio is not None:
|
|
audio_parts.append(blend_wave)
|
|
audio_parts.append(body_wave["waveform"])
|
|
if is_last:
|
|
# Last clip: emit body+tail raw after its boundary blend.
|
|
image_parts.append(torch.cat([body_img, tail_img], dim=0))
|
|
if audio is not None:
|
|
audio_parts.append(tail_wave["waveform"])
|
|
else:
|
|
image_parts.append(body_img)
|
|
prev_tail_img = tail_img
|
|
prev_tail_wave = tail_wave
|
|
|
|
kept_frames = decoded_frames - (overlap if not is_last else 0)
|
|
audio_sec = (0.0 if audio is None else
|
|
audio["waveform"].shape[-1] / float(audio["sample_rate"]))
|
|
report_lines.append("clip_%05d: decoded=%d frames, crossfade=%d frames "
|
|
"(%.4fs), kept=%d, audio=%.4fs"
|
|
% (idx, decoded_frames, overlap, overlap / float(fps),
|
|
kept_frames, audio_sec))
|
|
|
|
# Advance the green progress bar once this clip is fully decoded and
|
|
# its parts have been appended to the stitched timeline.
|
|
pbar.update_absolute(pos + 1, total_count)
|
|
|
|
del images
|
|
if audio is not None:
|
|
del audio
|
|
|
|
final_images = torch.cat(image_parts, dim=0).contiguous()
|
|
del image_parts
|
|
|
|
final_audio = None
|
|
if audio_parts:
|
|
final_waveform = torch.cat(audio_parts, dim=-1).contiguous()
|
|
del audio_parts
|
|
final_audio = {"waveform": final_waveform, "sample_rate": int(target_sr)}
|
|
|
|
frame_count = int(final_images.shape[0])
|
|
video_seconds = frame_count / float(fps)
|
|
audio_seconds = (final_audio["waveform"].shape[-1]
|
|
/ float(final_audio["sample_rate"])
|
|
if final_audio is not None else 0.0)
|
|
|
|
report_lines.append("TOTAL: %d frames = %.4fs at %.3f fps; audio=%.4fs%s"
|
|
% (frame_count, video_seconds, float(fps), audio_seconds,
|
|
"" if final_audio is not None
|
|
else " (no audio_vae connected)"))
|
|
report = "\n".join(report_lines)
|
|
log_.info("H3 clip stitcher finished: %d frames (%.3fs), audio %.3fs",
|
|
frame_count, video_seconds, audio_seconds)
|
|
|
|
return final_images, final_audio, frame_count, report
|
|
|
|
|
|
# --- Contrast measurement helpers for inline H3ClipRefiner ---
|
|
|
|
def _contrast_measure_luminance(frames):
|
|
"""Compute per-frame luminance contrast (std of luminance) for [N,H,W,3] frames in 0..1.
|
|
Returns [N] tensor of contrast values."""
|
|
# Luminance = 0.2126*R + 0.7152*G + 0.0722*B
|
|
lum = (frames[..., 0] * 0.2126 + frames[..., 1] * 0.7152 + frames[..., 2] * 0.0722)
|
|
# Contrast = std of luminance per frame
|
|
return lum.reshape(lum.shape[0], -1).std(dim=1)
|
|
|
|
|
|
def _contrast_correct_frames(frames, head_contrast, tail_contrast, strength):
|
|
"""Apply contrast correction to frames to undo the contrast drift.
|
|
|
|
The denoiser typically changes contrast from head to tail (usually increases it).
|
|
We measure the contrast ratio (tail/head) and scale luminance to bring tail
|
|
contrast back to head level.
|
|
|
|
frames: [N,H,W,3] in 0..1
|
|
head_contrast: mean contrast of first N frames
|
|
tail_contrast: mean contrast of last N frames
|
|
strength: 0..1, how much of the correction to apply (1.0 = full correction to head level)
|
|
Returns corrected frames.
|
|
"""
|
|
if head_contrast <= 1e-6 or tail_contrast <= 1e-6:
|
|
return frames
|
|
|
|
# Contrast ratio: tail/head.
|
|
# ratio > 1 -> contrast increases (degradation), need to reduce
|
|
# ratio < 1 -> contrast drops, need to boost
|
|
ratio = tail_contrast / head_contrast
|
|
|
|
# Target correction factor: 1/ratio brings tail contrast to head level.
|
|
# Interpolate between 1.0 (no correction) and 1/ratio (full correction).
|
|
correction = 1.0 + (1.0 / ratio - 1.0) * strength
|
|
correction = max(0.5, min(2.0, correction)) # clamp for safety
|
|
|
|
# Apply contrast correction in luminance space
|
|
# Convert to luminance, scale around mean, convert back
|
|
lum = (frames[..., 0] * 0.2126 + frames[..., 1] * 0.7152 + frames[..., 2] * 0.0722)
|
|
lum_mean = lum.mean(dim=(1, 2), keepdim=True)
|
|
lum_corrected = lum_mean + (lum - lum_mean) * correction
|
|
|
|
# Reconstruct RGB: preserve chroma by scaling RGB proportionally
|
|
# This is an approximation - proper way would be Lab space
|
|
scale = torch.where(lum > 1e-6, lum_corrected / lum, torch.ones_like(lum))
|
|
scale = scale.unsqueeze(-1)
|
|
corrected = frames * scale
|
|
|
|
return corrected.clamp_(0.0, 1.0)
|
|
|
|
|
|
class H3ClipRefiner:
|
|
""" Inline texture-ratchet correction node for H3 Motion Context clips.
|
|
|
|
Place this node BETWEEN the sampler (SamplerCustomAdvanced) and the
|
|
H3 Motion Context Save Latent node. It operates on the LATENT level
|
|
(no VAE decode/encode needed for measurement) to fix the texture ratchet:
|
|
high-band noise/grain that increases monotonically at each join.
|
|
|
|
The degradation is a "texture ratchet": high-band noise/grain increases
|
|
monotonically at each join (+4.2% mid-band per join). The statistic is
|
|
band_ratio = high-band std / total std. Measured across a chain it goes
|
|
0.3643 -> 0.3673 -> 0.3702 (monotone increase).
|
|
|
|
WORKFLOW:
|
|
1. First run: set target_ratio=0.0 (measure mode). The node measures the
|
|
band_ratio (high-band std / total std) of the video latent and logs it
|
|
(e.g., "band_ratio=0.3673"). The latent passes through unchanged.
|
|
2. Note the reported band_ratio from the FIRST clip (e.g., 0.3643).
|
|
3. Subsequent runs: set target_ratio to the first clip's band_ratio (e.g.,
|
|
0.3643). The node applies match_band to rescale the high-band so the
|
|
band_ratio matches the target, fixing the texture ratchet.
|
|
|
|
The correction uses match_band: rescales ONLY the high-frequency band to
|
|
match the target band_ratio. Low-frequency structure (scene content) is
|
|
bit-identical preserved. No VAE decode/encode needed for the correction.
|
|
|
|
TO MAKE IT LESS GRAINY: If a clip measures at 0.3917 and you want it less
|
|
grainy, set target_ratio to a LOWER value (e.g., 0.3800). The lower the
|
|
target_ratio, the more the high-band grain is reduced.
|
|
"""
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {"required": {"latent": ("LATENT", {
|
|
"tooltip": "The generated AV latent from your H3 sampler "
|
|
"(SamplerCustomAdvanced output)."}),
|
|
"target_ratio": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0,
|
|
"step": 0.0001,
|
|
"tooltip": "0.0 = measure only (report band_ratio, pass latent through).\n"
|
|
">0.0 = target band_ratio to match. Set to the first clip's "
|
|
"band_ratio (e.g., 0.3643) to fix the texture ratchet.\n"
|
|
"To make it LESS grainy, set target_ratio LOWER than the measured "
|
|
"band_ratio (e.g., measured 0.3917 -> set 0.3800)."}),
|
|
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 3.0,
|
|
"step": 0.01,
|
|
"tooltip": "Correction strength. 0.0 = measure only (pass through).\n"
|
|
"1.0 = full correction to target_ratio.\n"
|
|
">1.0 = over-correction (stronger grain reduction).\n"
|
|
"Use >1.0 if target_ratio alone doesn't reduce grain enough."}),
|
|
},
|
|
"optional": {"reference_latent": ("LATENT", {
|
|
"tooltip": "Optional: reference latent (e.g., first clip) to measure "
|
|
"target_ratio from automatically. If connected, target_ratio "
|
|
"is ignored and measured from this latent."})},
|
|
}
|
|
|
|
RETURN_TYPES = ("LATENT", "STRING")
|
|
RETURN_NAMES = ("latent", "report")
|
|
FUNCTION = "refine"
|
|
CATEGORY = "noEmbryo/MiniMax H3"
|
|
DESCRIPTION = ("Inline texture-ratchet corrector for H3 Motion Context clips.\nPlace "
|
|
"between SamplerCustomAdvanced and H3 Motion Context Save Latent.\n"
|
|
"Fixes the texture ratchet: high-band noise/grain that increases at "
|
|
"each join.\ntarget_ratio=0: measures band_ratio "
|
|
"(high-band std / total std), passes latent through.\n"
|
|
"target_ratio>0: applies match_band to rescale high-band to target "
|
|
"ratio.\nstrength=0: measure only. strength=1: full correction. >1: "
|
|
"over-correction.\nRun first clip with target_ratio=0 to measure its "
|
|
"band_ratio, then set target_ratio to that value for all subsequent "
|
|
"clips.\nTo make it LESS grainy, set target_ratio LOWER than measured "
|
|
"band_ratio.\nUse strength>1.0 for over-correction if needed.")
|
|
|
|
# noinspection PyUnusedLocal
|
|
@classmethod
|
|
def IS_CHANGED(cls, latent, target_ratio, strength,
|
|
reference_latent=None):
|
|
# noinspection PyBroadException
|
|
try:
|
|
import hashlib
|
|
latent_bytes = str(latent).encode()
|
|
latent_hash = hashlib.md5(latent_bytes).hexdigest()[:16]
|
|
return latent_hash, float(target_ratio), float(strength)
|
|
except Exception:
|
|
return float("NaN")
|
|
|
|
@staticmethod
|
|
def refine(latent, target_ratio, strength,
|
|
reference_latent=None):
|
|
# Extract video latent from AV latent
|
|
if not isinstance(latent, dict) or "samples" not in latent:
|
|
raise ValueError("H3ClipRefiner: expected a latent dict with 'samples' key")
|
|
|
|
samples = latent["samples"]
|
|
if hasattr(samples, "unbind"):
|
|
parts = list(samples.unbind())
|
|
elif isinstance(samples, (tuple, list)):
|
|
parts = list(samples)
|
|
else:
|
|
raise ValueError("H3ClipRefiner: expected AV latent with video+audio")
|
|
|
|
if len(parts) < 2:
|
|
raise ValueError("H3ClipRefiner: latent has no audio stream")
|
|
|
|
video_latent = parts[0].detach().to("cpu", copy=True).contiguous()
|
|
audio_latent = parts[1].detach().to("cpu", copy=True).contiguous()
|
|
|
|
# Handle video latent shape: [B,C,T,H,W] or [C,T,H,W]
|
|
if video_latent.dim() == 5:
|
|
video_latent = video_latent.squeeze(0) # [C,T,H,W]
|
|
# Now [C,T,H,W] - we need to process each channel/frame
|
|
# The band functions work on [..., H, W] so we process per channel-frame
|
|
|
|
# Measure band_ratio of the video latent
|
|
# Flatten batch/channel/time dims for band_ratio measurement
|
|
# video_latent is [C, T, H, W] -> reshape to [C*T, H, W] for band_ratio
|
|
c, t, h, w = video_latent.shape
|
|
flat_latent = video_latent.reshape(c * t, h, w)
|
|
|
|
# Measure band_ratio with improved averaging over multiple sigma values
|
|
# This gives a more robust measurement by averaging over multiple scales
|
|
# Use hardcoded best defaults: sigma=2.0, clamp=(0.5, 2.0)
|
|
sigmas = [1.0, 2.0, 3.0]
|
|
band_ratios = []
|
|
for s in sigmas:
|
|
br = _band_ratio(flat_latent, sigma=s)
|
|
if br is not None:
|
|
band_ratios.append(br)
|
|
|
|
if not band_ratios:
|
|
raise ValueError("H3ClipRefiner: could not measure band_ratio (latent too small?)")
|
|
|
|
band_ratio = sum(band_ratios) / len(band_ratios)
|
|
|
|
# Determine target_ratio
|
|
if reference_latent is not None:
|
|
# Measure target_ratio from reference latent
|
|
if not isinstance(reference_latent, dict) or "samples" not in reference_latent:
|
|
raise ValueError("H3ClipRefiner: reference_latent must be an AV latent dict")
|
|
ref_samples = reference_latent["samples"]
|
|
if hasattr(ref_samples, "unbind"):
|
|
ref_parts = list(ref_samples.unbind())
|
|
elif isinstance(ref_samples, (tuple, list)):
|
|
ref_parts = list(ref_samples)
|
|
else:
|
|
raise ValueError("H3ClipRefiner: reference_latent must be AV latent")
|
|
if len(ref_parts) < 2:
|
|
raise ValueError("H3ClipRefiner: reference_latent has no audio stream")
|
|
ref_video = ref_parts[0].detach().to("cpu", copy=True).contiguous()
|
|
if ref_video.dim() == 5:
|
|
ref_video = ref_video.squeeze(0)
|
|
rc, rt, rh, rw = ref_video.shape
|
|
ref_flat = ref_video.reshape(rc * rt, rh, rw)
|
|
|
|
# Measure reference band_ratio with same averaging
|
|
ref_ratios = []
|
|
for s in [1.0, 2.0, 3.0]:
|
|
br = _band_ratio(ref_flat, sigma=s)
|
|
if br is not None:
|
|
ref_ratios.append(br)
|
|
if not ref_ratios:
|
|
raise ValueError("H3ClipRefiner: could not measure target_ratio from "
|
|
"reference_latent")
|
|
target_ratio = sum(ref_ratios) / len(ref_ratios)
|
|
log_.info("H3ClipRefiner: measured target_ratio=%.4f from reference_latent",
|
|
target_ratio)
|
|
|
|
# Log the measurement
|
|
log_.info("H3ClipRefiner: clip band_ratio=%.4f (avg of %d sigmas), target_ratio=%.4f",
|
|
band_ratio, len(band_ratios), target_ratio if target_ratio > 0 else 0.0)
|
|
|
|
report = (f"H3ClipRefiner: band_ratio={band_ratio:.4f}"
|
|
f"{f', target_ratio={target_ratio:.4f}' if target_ratio > 0 else ''}")
|
|
|
|
if target_ratio <= 0.0:
|
|
# Measure only: report band_ratio, pass latent through
|
|
report += (" [MEASURE ONLY - latent passed through. Set target_ratio to this "
|
|
"clip's band_ratio to fix texture ratchet.]")
|
|
return latent, report
|
|
|
|
# Apply match_band to rescale high-band to target_ratio
|
|
# Use hardcoded best defaults: sigma=2.0, clamp=(0.5, 2.0)
|
|
corrected_flat, gain = _match_band(flat_latent, target_ratio, sigma=2.0,
|
|
clamp=(0.5, 2.0))
|
|
|
|
if gain is None:
|
|
report += " [MATCH_BAND FAILED - latent passed through unchanged]"
|
|
log_.warning("H3ClipRefiner: match_band failed, passing latent through unchanged")
|
|
return latent, report
|
|
|
|
# Apply strength: interpolate between original and corrected
|
|
# strength=0: original, strength=1: fully corrected, >1: over-correction
|
|
if strength <= 0.0:
|
|
# Measure only
|
|
report += (" [MEASURE ONLY - latent passed through. Set target_ratio to this "
|
|
"clip's band_ratio to fix texture ratchet.]")
|
|
return latent, report
|
|
elif strength < 1.0:
|
|
corrected_flat = flat_latent + (corrected_flat - flat_latent) * strength
|
|
effective_gain = 1.0 + (gain - 1.0) * strength
|
|
else:
|
|
# strength >= 1.0: full correction or over-correction
|
|
# Extrapolate beyond the corrected latent for over-correction
|
|
corrected_flat = flat_latent + (corrected_flat - flat_latent) * strength
|
|
effective_gain = 1.0 + (gain - 1.0) * strength
|
|
|
|
# Reshape back to [C, T, H, W]
|
|
corrected_video = corrected_flat.reshape(c, t, h, w)
|
|
|
|
# Reconstruct AV latent with corrected video + original audio
|
|
out = dict(latent)
|
|
# Restore batch dim if original had it
|
|
if parts[0].dim() == 5:
|
|
corrected_video = corrected_video.unsqueeze(0)
|
|
out["samples"] = _AVStreamPair(corrected_video, audio_latent)
|
|
|
|
report += (f" [CORRECTED - gain={effective_gain:.4f},"
|
|
f" band_ratio {band_ratio:.4f} -> {target_ratio:.4f}, "
|
|
f"strength={strength:.2f}]")
|
|
log_.info("H3ClipRefiner: applied match_band, gain=%.4f, "
|
|
"band_ratio %.4f -> %.4f, strength=%.2f",
|
|
effective_gain, band_ratio, target_ratio, strength)
|
|
|
|
return out, report
|
|
|
|
|
|
class H3MotionContextClipPurge:
|
|
""" Delete the saved H3 Motion Context clip archive files from a folder.
|
|
"""
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {"required": {"mode": ("BOOLEAN", {"default": True,
|
|
"label_on": "Purge", "label_off": "Preview (dry run)",
|
|
"tooltip": "Purge (Enabled): delete the matching files.\n"
|
|
"Preview (dry run, Disabled): delete nothing; the report "
|
|
"just lists the files that would be deleted."}),
|
|
"folder": ("STRING", {"default": "h3_context",
|
|
"tooltip": "Folder whose root-level clip archives will be deleted.\n"
|
|
"Absolute paths and paths relative to ComfyUI/output are "
|
|
"accepted."}),
|
|
"pattern": ("STRING", {"default": "clip_*.safetensors",
|
|
"tooltip": "Filename glob. Only root-level FILES matching this "
|
|
"pattern are deleted.\nSub-folders are never touched."}), },
|
|
"hidden": {"mode": "BOOLEAN"}}
|
|
|
|
RETURN_TYPES = ("STRING",)
|
|
RETURN_NAMES = ("report",)
|
|
FUNCTION = "purge"
|
|
CATEGORY = "noEmbryo/MiniMax H3"
|
|
OUTPUT_NODE = True
|
|
DESCRIPTION = ("Deletes the numbered h3_motion_context_av_v1 clip archive files "
|
|
"at the root of a folder (default: h3_context).\n"
|
|
"Purge (Enabled): deletes the files.\n"
|
|
"Preview (Disabled): dry run - the report only lists what would "
|
|
"be deleted.\nOnly files matching the pattern are removed; "
|
|
"sub-folders and everything inside them are left untouched.")
|
|
|
|
# noinspection PyUnusedLocal
|
|
@classmethod
|
|
def IS_CHANGED(cls, mode, folder, pattern):
|
|
return float("NaN")
|
|
|
|
@staticmethod
|
|
def purge(mode, folder, pattern):
|
|
d = _resolve_folder(folder)
|
|
pattern = (pattern or "clip_*.safetensors").strip()
|
|
|
|
doomed = []
|
|
for entry in os.scandir(d):
|
|
if entry.is_file(follow_symlinks=False) and not entry.is_dir():
|
|
if fnmatch.fnmatch(entry.name, pattern):
|
|
doomed.append((entry.name, entry.stat().st_size))
|
|
|
|
if not mode: # Preview (dry run)
|
|
lines = ["H3 clip purge (DRY RUN) in %s - nothing was deleted:" % d]
|
|
lines += [" would delete: %s (%s)" % (name, _fmt_size(size))
|
|
for name, size in doomed] or [" no matching files."]
|
|
lines.append("TOTAL: %d file(s), %s" %
|
|
(len(doomed), _fmt_size(sum(s for _, s in doomed))))
|
|
report = "\n".join(lines)
|
|
log_.info(report)
|
|
return (report,)
|
|
|
|
deleted = 0
|
|
freed = 0
|
|
lines = ["H3 clip purge in %s:" % d]
|
|
for name, size in doomed:
|
|
try:
|
|
os.remove(os.path.join(d, name))
|
|
deleted += 1
|
|
freed += size
|
|
lines.append(" deleted: %s (%s)" % (name, _fmt_size(size)))
|
|
except OSError as e:
|
|
lines.append(" FAILED to delete %s: %s" % (name, e))
|
|
if not deleted and not doomed:
|
|
lines.append(" no matching files.")
|
|
lines.append("TOTAL: deleted %d file(s), freed %s" %
|
|
(deleted, _fmt_size(freed)))
|
|
report = "\n".join(lines)
|
|
log_.info(report)
|
|
return (report,)
|
|
|
|
|
|
class H3ContextLatentConverter:
|
|
""" Convert an H3 Motion Context archive latent (as loaded by
|
|
MiniMaxH3MotionContextLoadLatent, whose 'samples' is a plain list) into
|
|
the AV latent form that comfy-core's LTXVSeparateAVLatent expects
|
|
(av_latent["samples"].unbind() -> (video, audio)).
|
|
"""
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {"required": {"latent": ("LATENT", {
|
|
"tooltip": "An H3 AV latent, e.g. the output of "
|
|
"MiniMaxH3MotionContextLoadLatent. Its 'samples' must be a "
|
|
"NestedTensor or a (video, audio) pair."})}}
|
|
|
|
RETURN_TYPES = ("LATENT",)
|
|
RETURN_NAMES = ("latent",)
|
|
FUNCTION = "convert"
|
|
CATEGORY = "noEmbryo/MiniMax H3"
|
|
DESCRIPTION = ("Repackages the AV latent loaded from an H3 Motion Context clip "
|
|
"archive into the nested (video, audio) form that "
|
|
"LTXVSeparateAVLatent expects, so saved clips can be re-sampled, "
|
|
"upscaled, or re-saved.")
|
|
|
|
@staticmethod
|
|
def convert(latent):
|
|
if not isinstance(latent, dict) or "samples" not in latent:
|
|
raise ValueError("h3_context_latent_converter: expected a latent dict with "
|
|
"a 'samples' key, got %r" % type(latent))
|
|
|
|
out = dict(latent)
|
|
samples = latent["samples"]
|
|
|
|
if hasattr(samples, "unbind"):
|
|
parts = list(samples.unbind())
|
|
elif isinstance(samples, (tuple, list)):
|
|
parts = list(samples)
|
|
else:
|
|
raise ValueError("h3_context_latent_converter: 'samples' is neither "
|
|
"unbindable nor a (video, audio) pair, got %r"
|
|
% type(samples))
|
|
|
|
if len(parts) < 2:
|
|
raise ValueError("h3_context_latent_converter: latent has no audio "
|
|
"stream (only %d part(s)); expected an H3 AV latent."
|
|
% len(parts))
|
|
|
|
expected_ndim = {"video": (4, 5), "audio": (3, 4)}
|
|
names = ("video", "audio")
|
|
dense = []
|
|
for name, t in zip(names, parts[:2]):
|
|
if t.ndim not in expected_ndim[name]:
|
|
raise ValueError("h3_context_latent_converter: %s stream has "
|
|
"unexpected shape %s." % (name, tuple(t.shape)))
|
|
if not torch.is_floating_point(t):
|
|
raise ValueError("h3_context_latent_converter: %s stream is not a "
|
|
"float tensor (dtype %s)." % (name, t.dtype))
|
|
# Force a real, dense, detached CPU copy: views into packed storage
|
|
# (or tensors still carrying nested metadata) can make VAE CUDA
|
|
# kernels crash with cudaErrorIllegalAddress.
|
|
dense.append(t.detach().to("cpu", copy=True).contiguous())
|
|
|
|
converted = {k: v for k, v in out.items() if k != "samples"}
|
|
converted["samples"] = _AVStreamPair(dense[0], dense[1])
|
|
return (converted,)
|
|
|
|
|
|
class H3AVLatentFromVideo:
|
|
""" Encode loaded video frames, or wrap an already-encoded latent, into an
|
|
H3 Motion Context AV latent suitable for saving.
|
|
"""
|
|
H3_FPS = 24.0
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {"required": {
|
|
"video_vae": ("VAE", {
|
|
"tooltip": "MiniMax H3 video VAE (FP16 or INT8 ConvRot)."}),
|
|
"audio_vae": ("VAE", {
|
|
"tooltip": "MiniMax H3 audio VAE FP32."}),
|
|
"source_fps": ("FLOAT", {
|
|
"default": 24.0, "min": 1.0, "max": 240.0, "step": 0.001,
|
|
"tooltip": "The frame rate of the loaded video. Frames are "
|
|
"resampled to H3's native 24 fps by time-based "
|
|
"frame picking, so audio stays in sync at any "
|
|
"source rate."}),
|
|
},
|
|
"optional": {
|
|
"images": ("IMAGE", {
|
|
"tooltip": "The whole video as frames (e.g. from VHS Load Video). "
|
|
"Leave un-connected when using the latent input."}),
|
|
"latent": ("LATENT", {
|
|
"tooltip": "Optional alternative to images. Accepts either a "
|
|
"nested AV latent from LTXVConcatAVLatent (its audio "
|
|
"stream is used directly) or an already-encoded H3 "
|
|
"video LATENT from VAE Encode. A standard [B,C,H,W] "
|
|
"latent is wrapped as a one-frame H3 video stream; "
|
|
"connect AUDIO separately when it has no audio stream."}),
|
|
"audio": ("AUDIO", {
|
|
"tooltip": "The video's audio (e.g. from VHS Load Video). "
|
|
"Leave un-connected for a silent clip. Ignored when "
|
|
"latent already contains an audio stream."}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("LATENT",)
|
|
RETURN_NAMES = ("latent",)
|
|
FUNCTION = "encode"
|
|
CATEGORY = "noEmbryo/MiniMax H3"
|
|
DESCRIPTION = ("Encodes a whole video (IMAGE frames + AUDIO) with the MiniMax H3 "
|
|
"VAEs into an AV latent that can be saved with 'H3 Motion Context "
|
|
"Save Latent' and stitched into later generations.")
|
|
|
|
@staticmethod
|
|
def _resample_frames(images, source_fps, target_fps):
|
|
""" Time-based frame picking, mirroring VHS's force_rate behavior.
|
|
"""
|
|
total = int(images.shape[0])
|
|
if abs(float(source_fps) - float(target_fps)) < 1e-6:
|
|
return images
|
|
duration = total / float(source_fps)
|
|
out_count = max(1, int(round(duration * float(target_fps))))
|
|
idx = [min(total - 1, max(0, int(round(k * float(source_fps)
|
|
/ float(target_fps)))))
|
|
for k in range(out_count)]
|
|
return images[idx]
|
|
|
|
@staticmethod
|
|
def _is_nested_av_latent(latent):
|
|
if not isinstance(latent, dict) or "samples" not in latent:
|
|
return False
|
|
samples = latent["samples"]
|
|
return (getattr(samples, "is_nested", False)
|
|
or isinstance(samples, (tuple, list))
|
|
or (hasattr(samples, "unbind") and not torch.is_tensor(samples)))
|
|
|
|
@staticmethod
|
|
def _video_from_ordinary_latent(latent):
|
|
if not isinstance(latent, dict) or "samples" not in latent:
|
|
raise ValueError("h3_av_latent_from_video: expected a LATENT dict "
|
|
"with a 'samples' tensor, got %r" % type(latent))
|
|
samples = latent["samples"]
|
|
if not torch.is_tensor(samples):
|
|
raise ValueError("h3_av_latent_from_video: ordinary latent input must "
|
|
"contain a tensor in 'samples', got %r"
|
|
% type(samples))
|
|
if samples.ndim == 4:
|
|
samples = samples.unsqueeze(2)
|
|
elif samples.ndim != 5:
|
|
raise ValueError("h3_av_latent_from_video: ordinary latent samples "
|
|
"must have shape [B,C,H,W] or [B,C,T,H,W], got %s."
|
|
% (tuple(samples.shape),))
|
|
if not torch.is_floating_point(samples):
|
|
raise ValueError("h3_av_latent_from_video: ordinary latent samples "
|
|
"are not a float tensor (dtype %s)." % (samples.dtype,))
|
|
return samples.detach().to("cpu", copy=True).contiguous()
|
|
|
|
@staticmethod
|
|
def _frame_count_from_video_latent(video_latent):
|
|
"""Return the canonical H3 pixel-frame count for a video latent."""
|
|
latent_t = int(video_latent.shape[2])
|
|
if latent_t <= 1:
|
|
return 1
|
|
if latent_t == 2:
|
|
return 5
|
|
return ((latent_t - 2) // 5) * 17 + 5
|
|
|
|
@staticmethod
|
|
def _encode_latent(latent, audio_vae, audio=None):
|
|
if H3AVLatentFromVideo._is_nested_av_latent(latent):
|
|
video_latent, audio_latent = _av_from_live_latent(latent)
|
|
source = "nested AV latent"
|
|
else:
|
|
video_latent = H3AVLatentFromVideo._video_from_ordinary_latent(latent)
|
|
if audio_vae is None:
|
|
raise ValueError("h3_av_latent_from_video: connect the MiniMax H3 "
|
|
"audio VAE to 'audio_vae' when using an ordinary "
|
|
"latent without an audio stream.")
|
|
if audio is not None:
|
|
audio = _resample_audio(
|
|
{"waveform": audio["waveform"][:1],
|
|
"sample_rate": int(audio["sample_rate"])},
|
|
int(getattr(audio_vae, "audio_sample_rate", 32000)))
|
|
audio_latent = audio_vae.encode(
|
|
audio["waveform"].movedim(1, -1))
|
|
else:
|
|
sr = int(getattr(audio_vae, "audio_sample_rate", 32000))
|
|
frame_count = H3AVLatentFromVideo._frame_count_from_video_latent(
|
|
video_latent)
|
|
silence = torch.zeros(
|
|
1, 1, max(1, int(round(frame_count
|
|
/ H3AVLatentFromVideo.H3_FPS * sr))))
|
|
audio_latent = audio_vae.encode(silence.movedim(1, -1))
|
|
audio_latent = audio_latent.detach().to("cpu", copy=True).contiguous()
|
|
source = "ordinary latent"
|
|
|
|
if int(video_latent.shape[0]) != 1:
|
|
raise ValueError("h3_av_latent_from_video: H3 Motion Context AV "
|
|
"latents must have batch size 1, got video shape %s."
|
|
% (tuple(video_latent.shape),))
|
|
if int(audio_latent.shape[0]) != 1:
|
|
raise ValueError("h3_av_latent_from_video: H3 Motion Context AV "
|
|
"latents must have batch size 1, got audio shape %s."
|
|
% (tuple(audio_latent.shape),))
|
|
|
|
out = {"samples": _AVStreamPair(video_latent, audio_latent)}
|
|
log_.info("H3 AV Latent from %s: video latent %s, audio latent %s",
|
|
source, tuple(video_latent.shape),
|
|
tuple(audio_latent.shape) if audio_latent is not None else None)
|
|
return (out,)
|
|
|
|
@staticmethod
|
|
def encode(images=None, video_vae=None, audio_vae=None, source_fps=None,
|
|
audio=None, latent=None):
|
|
if images is None and latent is None:
|
|
raise ValueError("h3_av_latent_from_video: connect either images or "
|
|
"latent.")
|
|
if images is not None and latent is not None:
|
|
raise ValueError("h3_av_latent_from_video: connect either images or "
|
|
"latent, not both.")
|
|
if latent is not None:
|
|
return H3AVLatentFromVideo._encode_latent(latent, audio_vae, audio)
|
|
if images is None:
|
|
raise ValueError("h3_av_latent_from_video: no frames to encode.")
|
|
if int(images.shape[0]) < 1:
|
|
raise ValueError("h3_av_latent_from_video: no frames to encode.")
|
|
if video_vae is None:
|
|
raise ValueError("h3_av_latent_from_video: connect the MiniMax H3 "
|
|
"video VAE to 'video_vae'.")
|
|
if audio_vae is None:
|
|
raise ValueError("h3_av_latent_from_video: connect the MiniMax H3 "
|
|
"audio VAE to 'audio_vae'.")
|
|
|
|
frames = H3AVLatentFromVideo._resample_frames(
|
|
images, source_fps, H3AVLatentFromVideo.H3_FPS)
|
|
frames = frames.to(video_vae.device if hasattr(video_vae, "device")
|
|
else "cpu", non_blocking=False)
|
|
video_latent = video_vae.encode(frames) # [B,C,T,H,W] (batch axis = time)
|
|
if getattr(video_latent, "ndim", 0) != 5:
|
|
raise ValueError("h3_av_latent_from_video: video encode returned shape "
|
|
"%s, expected [B,C,T,H,W]."
|
|
% (tuple(getattr(video_latent, "shape", ())),))
|
|
video_latent = video_latent.detach().to("cpu", copy=True).contiguous()
|
|
|
|
if audio is not None:
|
|
audio = _resample_audio(
|
|
{"waveform": audio["waveform"][:1],
|
|
"sample_rate": int(audio["sample_rate"])},
|
|
int(getattr(audio_vae, "audio_sample_rate", 32000)))
|
|
audio_latent = audio_vae.encode(
|
|
audio["waveform"].movedim(1, -1)) # [1,C,2,T]
|
|
audio_latent = audio_latent.detach().to("cpu", copy=True).contiguous()
|
|
else: # A silent clip still needs an audio stream for the archive format.
|
|
sr = int(getattr(audio_vae, "audio_sample_rate", 32000))
|
|
silence = torch.zeros(1, 1, max(1, int(round(frames.shape[0]
|
|
/ H3AVLatentFromVideo.H3_FPS
|
|
* sr))))
|
|
audio_latent = audio_vae.encode(silence.movedim(1, -1))
|
|
audio_latent = audio_latent.detach().to("cpu", copy=True).contiguous()
|
|
|
|
out = {"samples": _AVStreamPair(video_latent, audio_latent)}
|
|
log_.info("H3 AV Latent from Video: %d frames -> video latent %s, "
|
|
"audio latent %s", int(frames.shape[0]),
|
|
tuple(video_latent.shape),
|
|
tuple(audio_latent.shape) if audio_latent is not None else None)
|
|
return (out,)
|
|
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"H3MotionContextClipStitcher": H3MotionContextClipStitcher,
|
|
"H3ClipRefiner": H3ClipRefiner,
|
|
"H3ContextLatentConverter": H3ContextLatentConverter,
|
|
"H3MotionContextClipPurge": H3MotionContextClipPurge,
|
|
"H3AVLatentFromVideo": H3AVLatentFromVideo,
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"H3MotionContextClipStitcher": "H3 Motion Context Clip Stitcher",
|
|
"H3ClipRefiner": "H3 Clip Refiner",
|
|
"H3ContextLatentConverter": "H3 Context Latent Converter",
|
|
"H3MotionContextClipPurge": "H3 Motion Context Clip Purge",
|
|
"H3AVLatentFromVideo": "H3 AV Latent from Video",
|
|
}
|