| 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__))
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| device = mm.get_torch_device()
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| offload_device = mm.unet_offload_device()
|
|
|
| def linear_interpolation_fps(features, input_fps, output_fps, output_len=None):
|
| features = features.transpose(1, 2)
|
| 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
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| ed = frame0_idx + lt_i + 3
|
| wind_feat = torch.stack([
|
| audio_emb[i] if (0 <= i < audio_emb.shape[0]) else zero_audio_embed
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| 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": {
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| "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
|
|
|
| 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
|
|
|
|
|
| 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)
|
|
|
|
|
| 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]
|
| 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")
|
|
|
|
|
|
|
| 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)
|
|
|
|
|
| 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):
|
|
|
| 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",
|
| }
|
|
|
