import folder_paths import torch import torch.nn.functional as F import os import json import torchaudio from comfy.utils import load_torch_file, common_upscale import comfy.model_management as mm from accelerate import init_empty_weights from ..utils import set_module_tensor_to_device, log from ..nodes import WanVideoEncodeLatentBatch script_directory = os.path.dirname(os.path.abspath(__file__)) device = mm.get_torch_device() offload_device = mm.unet_offload_device() def linear_interpolation_fps(features, input_fps, output_fps, output_len=None): features = features.transpose(1, 2) # [1, C, T] seq_len = features.shape[2] / float(input_fps) if output_len is None: output_len = int(seq_len * output_fps) output_features = F.interpolate(features, size=output_len, align_corners=True, mode='linear') return output_features.transpose(1, 2) def get_audio_emb_window(audio_emb, frame_num, frame0_idx, audio_shift=2): zero_audio_embed = torch.zeros((audio_emb.shape[1], audio_emb.shape[2]), dtype=audio_emb.dtype, device=audio_emb.device) zero_audio_embed_3 = torch.zeros((3, audio_emb.shape[1], audio_emb.shape[2]), dtype=audio_emb.dtype, device=audio_emb.device) iter_ = 1 + (frame_num - 1) // 4 audio_emb_wind = [] for lt_i in range(iter_): if lt_i == 0: st = frame0_idx + lt_i - 2 ed = frame0_idx + lt_i + 3 wind_feat = torch.stack([ audio_emb[i] if (0 <= i < audio_emb.shape[0]) else zero_audio_embed for i in range(st, ed) ], dim=0) wind_feat = torch.cat((zero_audio_embed_3, wind_feat), dim=0) else: st = frame0_idx + 1 + 4 * (lt_i - 1) - audio_shift ed = frame0_idx + 1 + 4 * lt_i + audio_shift wind_feat = torch.stack([ audio_emb[i] if (0 <= i < audio_emb.shape[0]) else zero_audio_embed for i in range(st, ed) ], dim=0) audio_emb_wind.append(wind_feat) audio_emb_wind = torch.stack(audio_emb_wind, dim=0) return audio_emb_wind, ed - audio_shift class WhisperModelLoader: @classmethod def INPUT_TYPES(s): return { "required": { "model": (folder_paths.get_filename_list("audio_encoders"), {"tooltip": "These models are loaded from the 'ComfyUI/models/audio_encoders' folder",}), "base_precision": (["fp32", "bf16", "fp16"], {"default": "fp16"}), "load_device": (["main_device", "offload_device"], {"default": "main_device", "tooltip": "Initial device to load the model to, NOT recommended with the larger models unless you have 48GB+ VRAM"}), }, } RETURN_TYPES = ("WHISPERMODEL",) RETURN_NAMES = ("whisper_model", ) FUNCTION = "loadmodel" CATEGORY = "WanVideoWrapper" def loadmodel(self, model, base_precision, load_device): from transformers import WhisperConfig, WhisperModel, WhisperFeatureExtractor base_dtype = {"fp8_e4m3fn": torch.float8_e4m3fn, "fp8_e4m3fn_fast": torch.float8_e4m3fn, "bf16": torch.bfloat16, "fp16": torch.float16, "fp16_fast": torch.float16, "fp32": torch.float32}[base_precision] if load_device == "offload_device": transformer_load_device = offload_device else: transformer_load_device = device config_path = os.path.join(script_directory, "whisper_config.json") whisper_config = WhisperConfig(**json.load(open(config_path))) with init_empty_weights(): whisper = WhisperModel(whisper_config).eval() whisper.decoder = None # we only need the encoder feature_extractor_config = { "chunk_length": 30, "feature_extractor_type": "WhisperFeatureExtractor", "feature_size": 128, "hop_length": 160, "n_fft": 400, "n_samples": 480000, "nb_max_frames": 3000, "padding_side": "right", "padding_value": 0.0, "processor_class": "WhisperProcessor", "return_attention_mask": False, "sampling_rate": 16000 } feature_extractor = WhisperFeatureExtractor(**feature_extractor_config) model_path = folder_paths.get_full_path_or_raise("audio_encoders", model) sd = load_torch_file(model_path, device=transformer_load_device, safe_load=True) for name, param in whisper.named_parameters(): key = "model." + name value=sd[key] set_module_tensor_to_device(whisper, name, device=offload_device, dtype=base_dtype, value=value) whisper_model = { "feature_extractor": feature_extractor, "model": whisper, "dtype": base_dtype, } return (whisper_model,) class HuMoEmbeds: @classmethod def INPUT_TYPES(s): return {"required": { "num_frames": ("INT", {"default": 81, "min": -1, "max": 10000, "step": 1, "tooltip": "The total frame count to generate."}), "width": ("INT", {"default": 832, "min": 64, "max": 4096, "step": 16}), "height": ("INT", {"default": 480, "min": 64, "max": 4096, "step": 16}), "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: slower inference but more motion is allowed"}), "audio_start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The percent of the video to start applying audio conditioning"}), "audio_end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The percent of the video to stop applying audio conditioning"}) }, "optional" : { "whisper_model": ("WHISPERMODEL",), "vae": ("WANVAE", ), "reference_images": ("IMAGE", {"tooltip": "reference images for the humo model"}), "audio": ("AUDIO",), "tiled_vae": ("BOOLEAN", {"default": False, "tooltip": "Use tiled VAE encoding for reduced memory use"}), } } RETURN_TYPES = ("WANVIDIMAGE_EMBEDS", ) RETURN_NAMES = ("image_embeds", ) FUNCTION = "process" CATEGORY = "WanVideoWrapper" def process(self, num_frames, width, height, audio_scale, audio_cfg_scale, audio_start_percent, audio_end_percent, whisper_model=None, vae=None, reference_images=None, audio=None, tiled_vae=False): if reference_images is not None and vae is None: raise ValueError("VAE is required when reference images are provided") if whisper_model is None and audio is not None: raise ValueError("Whisper model is required when audio is provided") model = whisper_model["model"] feature_extractor = whisper_model["feature_extractor"] dtype = whisper_model["dtype"] sampling_rate = 16000 if audio is not None: audio_input = audio["waveform"][0] sample_rate = audio["sample_rate"] if sample_rate != sampling_rate: audio_input = torchaudio.functional.resample(audio_input, sample_rate, sampling_rate) if audio_input.shape[1] == 2: audio_input = audio_input.mean(dim=0, keepdim=False) else: audio_input = audio_input[0] model.to(device) audio_len = len(audio_input) // 640 # feature extraction audio_features = [] window = 750*640 for i in range(0, len(audio_input), window): audio_feature = feature_extractor(audio_input[i:i+window], sampling_rate=sampling_rate, return_tensors="pt").input_features audio_features.append(audio_feature) audio_features = torch.cat(audio_features, dim=-1).to(device, dtype) # preprocess window = 3000 audio_prompts = [] for i in range(0, audio_features.shape[-1], window): audio_prompt = model.encoder(audio_features[:,:,i:i+window], output_hidden_states=True).hidden_states audio_prompt = torch.stack(audio_prompt, dim=2) audio_prompts.append(audio_prompt) model.to(offload_device) audio_prompts = torch.cat(audio_prompts, dim=1) audio_prompts = audio_prompts[:,:audio_len*2] feat0 = linear_interpolation_fps(audio_prompts[:, :, 0: 8].mean(dim=2), 50, 25) feat1 = linear_interpolation_fps(audio_prompts[:, :, 8: 16].mean(dim=2), 50, 25) feat2 = linear_interpolation_fps(audio_prompts[:, :, 16: 24].mean(dim=2), 50, 25) feat3 = linear_interpolation_fps(audio_prompts[:, :, 24: 32].mean(dim=2), 50, 25) feat4 = linear_interpolation_fps(audio_prompts[:, :, 32], 50, 25) audio_emb = torch.stack([feat0, feat1, feat2, feat3, feat4], dim=2)[0] # [T, 5, 1280] else: audio_emb = torch.zeros(num_frames, 5, 1280, device=device) audio_len = num_frames pixel_frame_num = num_frames if num_frames != -1 else audio_len pixel_frame_num = 4 * ((pixel_frame_num - 1) // 4) + 1 latent_frame_num = (pixel_frame_num - 1) // 4 + 1 log.info(f"HuMo set to generate {pixel_frame_num} frames") #audio_emb, _ = get_audio_emb_window(audio_emb, pixel_frame_num, frame0_idx=0) num_refs = 0 if reference_images is not None: if reference_images.shape[1] != height or reference_images.shape[2] != width: reference_images_in = common_upscale(reference_images.movedim(-1, 1), width, height, "lanczos", "disabled").movedim(1, -1) else: reference_images_in = reference_images samples, = WanVideoEncodeLatentBatch.encode(self, vae, reference_images_in, tiled_vae, None, None, None, None) samples = samples["samples"].transpose(0, 2).squeeze(0) num_refs = samples.shape[1] vae.to(device) zero_frames = torch.zeros(1, 3, pixel_frame_num + 4*num_refs, height, width, device=device, dtype=vae.dtype) zero_latents = vae.encode(zero_frames, device=device, tiled=tiled_vae)[0].to(offload_device) vae.to(offload_device) mm.soft_empty_cache() target_shape = (16, latent_frame_num + num_refs, height // 8, width // 8) mask = torch.ones(4, target_shape[1], target_shape[2], target_shape[3], device=offload_device, dtype=vae.dtype) if reference_images is not None: mask[:,:-num_refs] = 0 image_cond = torch.cat([zero_latents[:, :(target_shape[1]-num_refs)], samples], dim=1) #zero_audio_pad = torch.zeros(num_refs, *audio_emb.shape[1:]).to(audio_emb.device) #audio_emb = torch.cat([audio_emb, zero_audio_pad], dim=0) else: image_cond = zero_latents mask = torch.zeros_like(mask) image_cond = torch.cat([mask, image_cond], dim=0) image_cond_neg = torch.cat([mask, zero_latents], dim=0) embeds = { "humo_audio_emb": audio_emb, "humo_audio_emb_neg": torch.zeros_like(audio_emb, dtype=audio_emb.dtype, device=audio_emb.device), "humo_image_cond": image_cond, "humo_image_cond_neg": image_cond_neg, "humo_reference_count": num_refs, "target_shape": target_shape, "num_frames": pixel_frame_num, "humo_audio_scale": audio_scale, "humo_audio_cfg_scale": audio_cfg_scale, "humo_start_percent": audio_start_percent, "humo_end_percent": audio_end_percent, } return (embeds, ) class WanVideoCombineEmbeds: @classmethod def INPUT_TYPES(s): return {"required": { "embeds_1": ("WANVIDIMAGE_EMBEDS",), "embeds_2": ("WANVIDIMAGE_EMBEDS",), } } RETURN_TYPES = ("WANVIDIMAGE_EMBEDS",) RETURN_NAMES = ("image_embeds",) FUNCTION = "add" CATEGORY = "WanVideoWrapper" EXPERIMENTAL = True def add(self, embeds_1, embeds_2): # Combine the two sets of embeds combined = {**embeds_1, **embeds_2} return (combined,) NODE_CLASS_MAPPINGS = { "WhisperModelLoader": WhisperModelLoader, "HuMoEmbeds": HuMoEmbeds, "WanVideoCombineEmbeds": WanVideoCombineEmbeds, } NODE_DISPLAY_NAME_MAPPINGS = { "WhisperModelLoader": "Whisper Model Loader", "HuMoEmbeds": "HuMo Embeds", "WanVideoCombineEmbeds": "WanVideo Combine Embeds", }