import torch import torch.nn.functional as F from ..utils import log import comfy.model_management as mm from comfy_api.latest import io device = mm.get_torch_device() offload_device = mm.unet_offload_device() class WanVideoLongCatAvatarExtendEmbeds(io.ComfyNode): @classmethod def define_schema(cls): return io.Schema( node_id="WanVideoLongCatAvatarExtendEmbeds", category="WanVideoWrapper", inputs=[ io.Latent.Input("prev_latents", tooltip="Full previous latents to be used to continue generation, continuation frames are selected based on 'overlap' parameter"), io.Custom("MULTITALK_EMBEDS").Input("audio_embeds", tooltip="Full length audio embeddings"), io.Int.Input("num_frames", default=93, min=1, max=256, step=1, tooltip="Number of new frames to generate"), io.Int.Input("overlap", default=13, min=0, max=16, step=1, tooltip="Number of overlapping frames from previous latents for video continuation, set to 0 for T2V"), io.Int.Input("frames_processed", default=0, min=0, max=10000, step=1, tooltip="Number of frames already processed in the video, used to select audio features"), io.Combo.Input("if_not_enough_audio", ["pad_with_start", "mirror_from_end"], default="pad_with_start", tooltip="What to do if there are not enough frames in pose_images for the window"), io.Int.Input("ref_frame_index", default=10, min=0, max=1000, step=1, tooltip="Values between 0 - 24 ensures better consistency, while selecting other ranges (e.g., -10 or 30) helps reduce repeated actions"), io.Int.Input("ref_mask_frame_range", default=3, min=0, max=20, step=1, tooltip="Larger range can further help mitigate repeated actions, but excessively large values may introduce artifacts"), io.Latent.Input("ref_latent", optional=True, tooltip="Reference latent used for consistency, generally should be either the init image, or first latent from first generation"), io.Latent.Input("samples", optional=True, tooltip="For the sampler 'samples' input, used for slicing samples per window for vid2vid"), io.Custom("IMAGE").Input("prev_images", optional=True, tooltip="LongCat-Avatar-1.5: decoded frames from the previous segment. When provided together with `vae`, the trailing `overlap` frames are re-encoded through the VAE and used as the overlap conditioning (matches v1.5's use_vcond=False behavior). Leave disconnected for v1.0."), io.Custom("WANVAE").Input("vae", optional=True, tooltip="LongCat-Avatar-1.5: VAE used to re-encode `prev_images` for the overlap region. Only used when `prev_images` is also provided."), ], outputs=[ io.Custom("WANVIDIMAGE_EMBEDS").Output(display_name="image_embeds", tooltip="Embeds for WanVideo LongCat Avatar generation"), io.Latent.Output(display_name="samples_slice", tooltip="Sliced latent samples for the new frames"), ], ) @classmethod def execute(cls, prev_latents, audio_embeds, num_frames, overlap, if_not_enough_audio, frames_processed, ref_frame_index, ref_mask_frame_range, ref_latent=None, samples=None, prev_images=None, vae=None) -> io.NodeOutput: new_audio_embed = audio_embeds.copy() audio_features = torch.stack(new_audio_embed["audio_features"]) num_audio_features = audio_features.shape[1] if audio_features.shape[1] < frames_processed + num_frames: deficit = frames_processed + num_frames - audio_features.shape[1] if if_not_enough_audio == "pad_with_start": pad = audio_features[:, :1].repeat(1, deficit, 1, 1) audio_features = torch.cat([audio_features, pad], dim=1) elif if_not_enough_audio == "mirror_from_end": to_add = audio_features[:, -deficit:, :].flip(dims=[1]) audio_features = torch.cat([audio_features, to_add], dim=1) log.warning(f"Not enough audio features, padded with strategy '{if_not_enough_audio}' from {num_audio_features} to {audio_features.shape[1]} frames") ref_target_masks = new_audio_embed.get("ref_target_masks", None) if ref_target_masks is not None: new_audio_embed["ref_target_masks"] = ref_target_masks[:, frames_processed:frames_processed+num_frames, :] prev_samples = prev_latents["samples"].clone() if overlap != 0: latent_overlap = (overlap - 1) // 4 + 1 if prev_images is not None and vae is not None: # LongCat-Avatar-1.5 path: re-encodes instead of just slicing img = prev_images[-overlap:] if img.shape[-1] == 4: img = img[..., :3] img = img.to(vae.dtype).to(device) * 2.0 - 1.0 img = img.permute(3, 0, 1, 2).unsqueeze(0).contiguous() # [T, H, W, C] -> [B, C, T, H, W] vae.to(device) prev_samples = vae.encode(img, device=device).to(prev_samples) vae.to(offload_device) mm.soft_empty_cache() log.info(f"Re-encoded {overlap} overlap frames -> latent shape {tuple(prev_samples.shape)}") else: prev_samples = prev_samples[:, :, -latent_overlap:] ref_sample = None if ref_latent is not None: ref_sample = ref_latent["samples"][0, :, :1].clone() log.info(f"Previous latents shape: {prev_samples.shape}, using last {latent_overlap} latent frames for overlap.") new_latent_frames = (num_frames - 1) // 4 + 1 target_shape = (16, new_latent_frames, prev_samples.shape[-2], prev_samples.shape[-1]) audio_stride = new_audio_embed.get("audio_stride", 2) indices = torch.arange(2 * 2 + 1) - 2 if frames_processed == 0: audio_start_idx = 0 else: audio_start_idx = (frames_processed - overlap) * audio_stride audio_end_idx = audio_start_idx + num_frames * audio_stride log.info(f"Extracting audio embeddings from index {audio_start_idx} to {audio_end_idx}") audio_embs = [] for human_idx in range(len(audio_features)): center_indices = torch.arange(audio_start_idx, audio_end_idx, audio_stride).unsqueeze(1) + indices.unsqueeze(0) center_indices = torch.clamp(center_indices, min=0, max=audio_features[human_idx].shape[0] - 1) audio_emb = audio_features[human_idx][center_indices].unsqueeze(0).to(device) audio_embs.append(audio_emb) audio_emb = torch.cat(audio_embs, dim=0) new_audio_embed["audio_features"] = None new_audio_embed["audio_emb_slice"] = audio_emb longcat_avatar_options = { "longcat_ref_latent": ref_sample, "ref_frame_index": ref_frame_index, "ref_mask_frame_range": ref_mask_frame_range, } embeds = { "target_shape": target_shape, "num_frames": num_frames, "extra_latents": [{"samples": prev_samples, "index": 0}] if overlap != 0 else None, "multitalk_embeds": new_audio_embed, "longcat_avatar_options": longcat_avatar_options, } samples_slice = None if samples is not None: latent_start_index = (frames_processed - 1) // 4 + 1 if frames_processed > 0 else 0 latent_end_index = latent_start_index + new_latent_frames samples_slice = samples.copy() samples_slice["samples"] = samples["samples"][:, :, latent_start_index:latent_end_index].clone() return io.NodeOutput(embeds, samples_slice) class LongCatAvatarWhisperEmbeds: """Audio embeds for LongCat-Video-Avatar-1.5 (Whisper-large-v3). Produces a MULTITALK_EMBEDS dict whose audio_features are shaped [T, 5, 1280] (5 grouped Whisper layers, 1280-d hidden state), matching the audio stream the v1.5 AudioProjModel expects. audio_stride is set to 1 to signal v1.5 timing to the consumer nodes (vs. 2 for the v1.0 wav2vec2 path). """ @classmethod def INPUT_TYPES(s): return { "required": { "whisper_model": ("WHISPERMODEL",), "audio_1": ("AUDIO",), "normalize_loudness": ("BOOLEAN", {"default": True, "tooltip": "Normalize audio loudness to -23 LUFS before encoding (matches the v1.5 reference pipeline)"}), "num_frames": ("INT", {"default": 93, "min": 1, "max": 10000, "step": 1, "tooltip": "Total frame count to generate; bounds how much audio is consumed"}), "fps": ("FLOAT", {"default": 25.0, "min": 1.0, "max": 60.0, "step": 0.1, "tooltip": "Target video fps. LongCat-Video-Avatar-1.5 is trained at 25 fps."}), "audio_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01, "tooltip": "Strength of the audio conditioning"}), "audio_cfg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01, "tooltip": "When not 1.0, an extra model pass without audio conditioning is done"}), "multi_audio_type": (["para", "add"], {"default": "para", "tooltip": "'para' overlays speakers in parallel (equal length); 'add' concatenates speakers sequentially with silence padding"}), }, "optional": { "audio_2": ("AUDIO",), "audio_3": ("AUDIO",), "audio_4": ("AUDIO",), "ref_target_masks": ("MASK", {"tooltip": "Per-speaker semantic mask(s) in pixel space, one per speaker"}), }, } RETURN_TYPES = ("MULTITALK_EMBEDS", "AUDIO", "INT",) RETURN_NAMES = ("multitalk_embeds", "audio", "num_frames",) FUNCTION = "process" CATEGORY = "WanVideoWrapper" def process(self, whisper_model, audio_1, normalize_loudness, num_frames, fps, audio_scale, audio_cfg_scale, multi_audio_type, audio_2=None, audio_3=None, audio_4=None, ref_target_masks=None): import torchaudio import numpy as np from ..multitalk.nodes import loudness_norm model = whisper_model["model"] feature_extractor = whisper_model["feature_extractor"] dtype = whisper_model["dtype"] sr = 16000 MEL_CHUNK = 750 * 640 # 480000 samples = 30s at 16kHz; matches Whisper's chunk_length ENC_CHUNK = 3000 # encoder window in mel frames ENC_FPS = 50 # whisper encoder output frames per second def linear_interp(features, output_len): features = features.transpose(1, 2) # [B, D, T] out = F.interpolate(features, size=output_len, align_corners=True, mode='linear') return out.transpose(1, 2) audio_inputs = [a for a in [audio_1, audio_2, audio_3, audio_4] if a is not None] audio_features_list = [] seq_lengths = [] audio_outputs = [] end_time = num_frames / float(fps) end_sample = int(end_time * sr) for audio in audio_inputs: audio_input = audio["waveform"] sample_rate = audio["sample_rate"] if sample_rate != sr: audio_input = torchaudio.functional.resample(audio_input, sample_rate, sr) audio_input = audio_input[0][0] audio_segment = audio_input[:end_sample].cpu().numpy().astype(np.float32) if normalize_loudness: audio_segment = loudness_norm(audio_segment, sr=sr) audio_duration = len(audio_segment) / sr video_length = int(audio_duration * fps) if video_length < 1: continue mel_chunks = [] for i in range(0, len(audio_segment), MEL_CHUNK): mel = feature_extractor(audio_segment[i:i + MEL_CHUNK], sampling_rate=sr, return_tensors="pt").input_features mel_chunks.append(mel) mel_features = torch.cat(mel_chunks, dim=-1).to(device=device, dtype=dtype) model.to(device) enc_chunks = [] with torch.no_grad(): for i in range(0, mel_features.shape[-1], ENC_CHUNK): chunk = mel_features[:, :, i:i + ENC_CHUNK] chunk_hs = model.encoder(chunk, output_hidden_states=True).hidden_states enc_chunks.append(torch.stack(chunk_hs, dim=2)) # [1, T_enc, n_layers+1, D] model.to(offload_device) audio_prompts = torch.cat(enc_chunks, dim=1) audio_prompts = audio_prompts[:, :video_length * 2] feat0 = linear_interp(audio_prompts[:, :, 0:8].mean(dim=2), video_length) feat1 = linear_interp(audio_prompts[:, :, 8:16].mean(dim=2), video_length) feat2 = linear_interp(audio_prompts[:, :, 16:24].mean(dim=2), video_length) feat3 = linear_interp(audio_prompts[:, :, 24:32].mean(dim=2), video_length) feat4 = linear_interp(audio_prompts[:, :, 32], video_length) audio_emb = torch.stack([feat0, feat1, feat2, feat3, feat4], dim=2)[0] # [T, 5, 1280] audio_features_list.append(audio_emb.cpu().detach()) seq_lengths.append(audio_emb.shape[0]) waveform_tensor = torch.from_numpy(audio_segment).float().unsqueeze(0).unsqueeze(0) audio_outputs.append({"waveform": waveform_tensor, "sample_rate": sr}) if len(audio_features_list) == 0: raise RuntimeError("No valid Whisper audio embeddings extracted, please check inputs") if len(audio_features_list) > 1: if multi_audio_type == "para": max_len = max(seq_lengths) padded = [] for emb in audio_features_list: if emb.shape[0] < max_len: pad = torch.zeros(max_len - emb.shape[0], *emb.shape[1:], dtype=emb.dtype) emb = torch.cat([emb, pad], dim=0) padded.append(emb) audio_features_list = padded else: # "add" total_len = sum(seq_lengths) full_list = [] offset = 0 for emb, length in zip(audio_features_list, seq_lengths): full = torch.zeros(total_len, *emb.shape[1:], dtype=emb.dtype) full[offset:offset + length] = emb full_list.append(full) offset += length audio_features_list = full_list multitalk_embeds = { "audio_features": audio_features_list, "audio_scale": audio_scale, "audio_cfg_scale": audio_cfg_scale, "ref_target_masks": ref_target_masks, "audio_stride": 1, "audio_encoder_type": "whisper", } if len(audio_outputs) == 1: out_audio = audio_outputs[0] elif multi_audio_type == "para": max_len = max(a["waveform"].shape[-1] for a in audio_outputs) mixed = torch.zeros(1, 1, max_len, dtype=audio_outputs[0]["waveform"].dtype) for a in audio_outputs: w = a["waveform"] if w.shape[-1] < max_len: w = F.pad(w, (0, max_len - w.shape[-1])) mixed += w out_audio = {"waveform": mixed, "sample_rate": sr} else: total_len = sum(a["waveform"].shape[-1] for a in audio_outputs) mixed = torch.zeros(1, 1, total_len, dtype=audio_outputs[0]["waveform"].dtype) offset = 0 for a in audio_outputs: w = a["waveform"] mixed[:, :, offset:offset + w.shape[-1]] += w offset += w.shape[-1] out_audio = {"waveform": mixed, "sample_rate": sr} return (multitalk_embeds, out_audio, num_frames) NODE_CLASS_MAPPINGS = { "WanVideoLongCatAvatarExtendEmbeds": WanVideoLongCatAvatarExtendEmbeds, "LongCatAvatarWhisperEmbeds": LongCatAvatarWhisperEmbeds, } NODE_DISPLAY_NAME_MAPPINGS = { "WanVideoLongCatAvatarExtendEmbeds": "WanVideo LongCat Avatar Extend Embeds", "LongCatAvatarWhisperEmbeds": "LongCat Avatar Whisper Embeds (v1.5)", }