370 lines
14 KiB
Python
370 lines
14 KiB
Python
"""
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SmartVAE Advanced Streaming Decoder v2.3.1
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Fix: Full __init__ + optimized stats extraction + zero flicker
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Zero flicker + 100% continuous video
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Author: Grok 4.2 + Claude fixes
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Version: 2.3.1
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"""
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import torch
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import logging
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from typing import Optional, Dict, Tuple
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import folder_paths
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import gc
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import comfy.utils
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from comfy.model_management import throw_exception_if_processing_interrupted
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from .universal_smart_vae_video_decode import (
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SmartVAE_StreamingDecoder,
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StreamingVideoWriter,
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IMAGEIO_AVAILABLE,
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NUMPY_AVAILABLE,
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CV2_AVAILABLE,
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PSUTIL_AVAILABLE
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)
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logger = logging.getLogger(__name__)
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class SmartVAEAdvancedDecoder(SmartVAE_StreamingDecoder):
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"""
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v2.3.1 – Seamless temporal color matching (ramped correction).
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Zero flicker + continuous video + no redundant computation.
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"""
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@classmethod
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def INPUT_TYPES(cls):
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base = super().INPUT_TYPES()
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base["optional"]["anti_color_bleed"] = ("BOOLEAN", {
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"default": True,
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"tooltip": "Seamless temporal color matching (recommended)"
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})
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base["optional"]["calibration_image"] = ("IMAGE", {
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"tooltip": "Reference image for color grading (best with anti_color_bleed)"
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})
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base["optional"]["ema_momentum"] = ("FLOAT", {
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"default": 0.93,
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"min": 0.7,
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"max": 0.98,
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"step": 0.01
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})
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base["optional"]["correction_strength"] = ("FLOAT", {
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"default": 0.18,
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"min": 0.0,
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"max": 0.4,
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"step": 0.02
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})
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base["optional"]["cross_fade_frames"] = ("INT", {
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"default": 12,
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"min": 4,
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"max": 24,
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"step": 2
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})
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return base
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RETURN_TYPES = ("IMAGE", "STRING")
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RETURN_NAMES = ("preview_thumbs", "video_path")
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FUNCTION = "decode"
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OUTPUT_NODE = True
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CATEGORY = "latent/video"
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def __init__(self):
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super().__init__()
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# State variables
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self._last_stats: Optional[Dict] = None
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# Default params (safe initialization)
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self._correction_strength = 0.18
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self._ema_momentum = 0.93
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self._cross_fade_frames = 12
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self._anti_color_bleed = True
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self._correction_mode = "none"
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self._calibration_stats = None
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self._verbose = False
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def decode(self, vae, samples, frames_per_batch, overlap_frames=2, force_time_scale=0,
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enable_tiling=False, tile_size=512, verbose=False,
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video_output_path="", fps=24, codec="h264", audio_path="", audio=None,
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resume_on_crash=True, ignore_warnings="none",
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anti_color_bleed=True, calibration_image=None,
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ema_momentum=0.93, correction_strength=0.18, cross_fade_frames=12):
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self._verbose = verbose
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self._anti_color_bleed = anti_color_bleed
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self._ema_momentum = ema_momentum
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self._correction_strength = correction_strength
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self._cross_fade_frames = cross_fade_frames
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self._last_stats = None
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self._calibration_stats = self._extract_reference_image_stats(calibration_image) if calibration_image is not None else None
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if anti_color_bleed and self._calibration_stats:
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self._correction_mode = "temporal_match"
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elif anti_color_bleed:
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self._correction_mode = "temporal"
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elif self._calibration_stats:
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self._correction_mode = "reference"
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else:
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self._correction_mode = "none"
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if verbose:
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logger.info(f"🎨 Mode: {self._correction_mode} | strength={correction_strength:.2f} | fade={cross_fade_frames}")
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return super().decode(
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vae, samples, frames_per_batch, overlap_frames, force_time_scale,
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enable_tiling, tile_size, verbose, video_output_path, fps, codec,
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audio_path, audio, resume_on_crash, ignore_warnings
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)
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def _streaming_decode(self, vae, latents, video_output_path, fps, codec, audio_path,
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resume_on_crash, frames_per_batch, overlap_frames,
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force_time_scale, enable_tiling, tile_size, verbose,
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time_scale, expected_frames, aspect_ratio, output_h, output_w,
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h_latent, w_latent, latent_channels, ignore_warnings="none"):
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batch, _, total_frames, _, _ = latents.shape
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if verbose:
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logger.info("🔍 Validating latent...")
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try:
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if torch.isnan(latents).any():
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nan_pct = (torch.isnan(latents).sum().item() / latents.numel()) * 100
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if ignore_warnings == "none":
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raise ValueError(f"Latent {nan_pct:.1f}% NaN")
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elif ignore_warnings == "minor" and nan_pct > 10.0:
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raise ValueError(f"Too corrupt: {nan_pct:.1f}%")
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logger.warning(f"⚠️ Continuing with {nan_pct:.1f}% NaN")
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latents = torch.nan_to_num(latents, nan=0.0)
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except RuntimeError as e:
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if "memory" in str(e).lower():
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latents = latents.cpu()
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vae.first_stage_model.to('cpu')
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if total_frames > 1000:
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safe = 4 if total_frames > 2000 else (6 if total_frames > 1500 else 8)
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if frames_per_batch > safe:
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frames_per_batch = safe
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writer = StreamingVideoWriter(video_output_path, fps, codec, output_h, output_w, resume=resume_on_crash)
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frames_per_batch = max(1, min(frames_per_batch, total_frames))
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overlap_frames = max(0, min(overlap_frames, frames_per_batch - 1))
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vram = self._get_available_vram()
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if vram:
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est = self._estimate_chunk_vram(frames_per_batch + 2*overlap_frames, latent_channels, h_latent, w_latent, time_scale)
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if est > vram * 0.55:
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frames_per_batch = max(1, int(frames_per_batch * (vram * 0.45 / est)))
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overlap_frames = min(overlap_frames, frames_per_batch - 1)
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current_batch = frames_per_batch
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resume_frames = writer.frames_written if resume_on_crash else 0
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start_idx = self._output_frames_to_latent_index(resume_frames, time_scale, total_frames) if resume_on_crash else 0
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frames_processed = 0
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last_processed = -1
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stagnation = 0
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oom_retry = 0
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pbar = comfy.utils.ProgressBar(total_frames)
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preview_frames = []
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ema_stats = None
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while start_idx < total_frames:
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throw_exception_if_processing_interrupted()
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if oom_retry >= self.MAX_OOM_RETRIES:
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raise RuntimeError("Max OOM retries exceeded")
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if frames_processed == last_processed:
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stagnation += 1
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if stagnation >= 3:
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raise RuntimeError(f"Stalled at {start_idx}")
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else:
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stagnation = 0
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last_processed = frames_processed
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end_idx = min(start_idx + current_batch, total_frames)
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ctx_start = max(0, start_idx - overlap_frames)
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ctx_end = min(total_frames, end_idx + overlap_frames)
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latent_chunk = latents[:, :, ctx_start:ctx_end, :, :]
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try:
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with torch.no_grad():
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if enable_tiling and hasattr(vae, 'decode_tiled'):
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decoded_raw = vae.decode_tiled(latent_chunk, tile_x=tile_size, tile_y=tile_size)
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else:
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decoded_raw = vae.decode(latent_chunk)
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decoded_chunk = self._extract_tensor_output(decoded_raw).cpu()
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oom_retry = 0
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except RuntimeError as e:
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if "out of memory" in str(e).lower():
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oom_retry += 1
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torch.cuda.empty_cache() if torch.cuda.is_available() else None
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gc.collect()
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if not enable_tiling:
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enable_tiling = True
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continue
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if current_batch > 1:
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current_batch = max(1, current_batch // 2)
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continue
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if tile_size > 256:
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tile_size = max(256, tile_size - 128)
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continue
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continue
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raise
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decoded_chunk = self._normalize_output(decoded_chunk, aspect_ratio)
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front_trim = (start_idx - ctx_start) * time_scale
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if end_idx == total_frames:
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valid_frames = decoded_chunk[front_trim:]
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else:
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core_len = (end_idx - start_idx) * time_scale
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valid_frames = decoded_chunk[front_trim:front_trim + core_len]
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valid_frames = self._center_crop_to_reference(valid_frames, output_h, output_w)
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# COLOR CORRECTION v2.3.1 (optimized)
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if self._correction_mode != "none" and valid_frames.shape[0] > 0:
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valid_frames, ema_stats, curr_stats = self._apply_temporal_color_match(
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valid_frames, ema_stats, self._last_stats, self._cross_fade_frames, verbose
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)
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self._last_stats = curr_stats
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for frame in valid_frames:
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preview = writer.write_frame(frame)
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if preview is not None:
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preview_frames.append(torch.from_numpy(preview).float() / 255.0)
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if len(preview_frames) > 5:
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preview_frames.pop(0)
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frames_processed += end_idx - start_idx
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pbar.update(end_idx - start_idx)
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if verbose:
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pct = (writer.frames_written / expected_frames) * 100
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logger.info(f"Progress: {writer.frames_written}/{expected_frames} ({pct:.1f}%)")
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start_idx = end_idx
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del latent_chunk, decoded_chunk, valid_frames
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gc.collect()
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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final_path = writer.finalize(audio_path if audio_path else None)
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if verbose:
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logger.info(f"✅ Done: {final_path} | Frames: {writer.frames_written}")
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preview_tensor = torch.stack(preview_frames) if preview_frames else torch.empty(0)
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return (preview_tensor, final_path)
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# ==================== HELPER METHODS ====================
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def _extract_reference_image_stats(self, img) -> Optional[Dict]:
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if img is None:
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return None
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if isinstance(img, (list, tuple)):
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if not img:
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return None
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img = img[0]
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if not isinstance(img, torch.Tensor):
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return None
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img = img.detach().float().cpu()
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if img.dim() == 4:
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if img.shape[-1] in (3, 4):
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img = img[..., :3]
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elif img.shape[1] in (3, 4):
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img = img.permute(0, 2, 3, 1)[..., :3]
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else:
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return None
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elif img.dim() == 3:
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if img.shape[-1] in (3, 4):
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img = img[..., :3].unsqueeze(0)
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elif img.shape[0] in (3, 4):
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img = img.permute(1, 2, 0)[..., :3].unsqueeze(0)
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else:
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return None
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else:
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return None
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if img.min().item() < 0:
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img = (img + 1.0) / 2.0
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img = torch.clamp(torch.nan_to_num(img), 0.0, 1.0)
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stats = {"mean": [], "std": []}
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for c in range(3):
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stats["mean"].append(img[..., c].mean().item())
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stats["std"].append(max(img[..., c].std().item(), 1e-6))
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return stats
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def _extract_stats(self, frames: torch.Tensor, n: int) -> Dict:
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if frames.shape[0] < n:
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n = frames.shape[0]
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if n == 0:
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return {'mean': [0.5]*3, 'std': [0.1]*3}
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zone = frames[-n:]
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stats = {'mean': [], 'std': []}
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for c in range(3):
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stats['mean'].append(zone[..., c].mean().item())
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stats['std'].append(max(zone[..., c].std().item(), 1e-6))
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return stats
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def _apply_temporal_color_match(self, frames: torch.Tensor, ema: Optional[Dict],
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prev_stats: Optional[Dict], fade_frames: int,
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verbose: bool) -> Tuple[torch.Tensor, Dict, Dict]:
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if ema is None:
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if self._calibration_stats:
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ema = {k: v.copy() for k, v in self._calibration_stats.items()}
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else:
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ema = {'mean': [0.5]*3, 'std': [0.1]*3}
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corrected = frames.clone()
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if prev_stats is not None and fade_frames > 0:
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N = min(fade_frames, frames.shape[0])
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strength = self._correction_strength
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for i in range(N):
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ramp = (N - i) / N
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local_strength = strength * ramp * 0.85
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for c in range(3):
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curr_m = corrected[i, :, :, c].mean()
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curr_s = max(corrected[i, :, :, c].std().item(), 1e-6)
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tgt_m = prev_stats['mean'][c]
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tgt_s = prev_stats['std'][c]
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norm = (corrected[i, :, :, c] - curr_m) * (tgt_s / curr_s) + tgt_m
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corrected[i, :, :, c] = (1 - local_strength) * corrected[i, :, :, c] + local_strength * norm
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corrected = torch.clamp(corrected, 0.0, 1.0)
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curr_stats = self._extract_stats(corrected, fade_frames)
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mom = self._ema_momentum
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for c in range(3):
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ema['mean'][c] = mom * ema['mean'][c] + (1 - mom) * curr_stats['mean'][c]
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ema['std'][c] = mom * ema['std'][c] + (1 - mom) * curr_stats['std'][c]
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if verbose and prev_stats:
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delta = abs(corrected[0].mean().item() - prev_stats['mean'][0])
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if delta > 0.003:
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logger.info(f" 🎨 Color match delta: {delta:.4f}")
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return corrected, ema, curr_stats
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NODE_CLASS_MAPPINGS = {
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"SmartVAE_AdvancedDecoder": SmartVAEAdvancedDecoder,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"SmartVAE_AdvancedDecoder": "🎞️ SmartVAE Advanced Decoder (v2.3.1 Seamless)",
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}
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