Aduio:Update to R2v V2

configs/audio_driven/wan_i2v_audio.json

@@ -11,7 +11,7 @@
     "cross_attn_2_type": "flash_attn3",
     "seed": 42,
     "sample_guide_scale":1,
-    "sample_shift": 6,
+    "sample_shift": 5,
     "enable_cfg": false,
     "cpu_offload": false,
     "use_tiling_vae": true

lightx2v/models/networks/wan/infer/audio/pre_wan_audio_infer.py

@@ -46,6 +46,7 @@ class WanAudioPreInfer(WanPreInfer):
             "timestep": t,
         }
         audio_dit_blocks.append(inputs["audio_adapter_pipe"](**audio_model_input))
+        ##audio_dit_blocks = None##Debug Drop Audio
 
         if positive:
             context = inputs["text_encoder_output"]["context"]

lightx2v/models/runners/wan/wan_audio_runner.py

@@ -19,6 +19,7 @@ from lightx2v.models.video_encoders.hf.wan.vae import WanVAE
 from lightx2v.models.networks.wan.audio_adapter import AudioAdapter, AudioAdapterPipe, rank0_load_state_dict_from_path
 
 from lightx2v.models.schedulers.wan.step_distill.scheduler import WanStepDistillScheduler
+from lightx2v.models.schedulers.wan.audio.scheduler import EulerSchedulerTimestepFix
 
 from loguru import logger
 import torch.distributed as dist
@@ -35,6 +36,45 @@ import warnings
 from typing import Optional, Tuple, Union
 
 
+def add_mask_to_frames(
+    frames: np.ndarray,
+    mask_rate: float = 0.1,
+    rnd_state: np.random.RandomState = None,
+) -> np.ndarray:
+    if mask_rate is None:
+        return frames
+
+    if rnd_state is None:
+        rnd_state = np.random.RandomState()
+
+    h, w = frames.shape[-2:]
+    mask = rnd_state.rand(h, w) > mask_rate
+    frames = frames * mask
+    return frames
+
+
+def add_noise_to_frames(
+    frames: np.ndarray,
+    noise_mean: float = -3.0,
+    noise_std: float = 0.5,
+    rnd_state: np.random.RandomState = None,
+) -> np.ndarray:
+    if noise_mean is None or noise_std is None:
+        return frames
+
+    if rnd_state is None:
+        rnd_state = np.random.RandomState()
+
+    shape = frames.shape
+    bs = 1 if len(shape) == 4 else shape[0]
+    sigma = rnd_state.normal(loc=noise_mean, scale=noise_std, size=(bs,))
+    sigma = np.exp(sigma)
+    sigma = np.expand_dims(sigma, axis=tuple(range(1, len(shape))))
+    noise = rnd_state.randn(*shape) * sigma
+    frames = frames + noise
+    return frames
+
+
 def get_crop_bbox(ori_h, ori_w, tgt_h, tgt_w):
     tgt_ar = tgt_h / tgt_w
     ori_ar = ori_h / ori_w
@@ -77,7 +117,12 @@ def adaptive_resize(img):
     aspect_ratios = np.array(np.array(list(bucket_config.keys())))
     closet_aspect_idx = np.argmin(np.abs(aspect_ratios - ori_ratio))
     closet_ratio = aspect_ratios[closet_aspect_idx]
-    target_h, target_w = 480, 832
+    if ori_ratio < 1.0:
+        target_h, target_w = 480, 832
+    elif ori_ratio == 1.0:
+        target_h, target_w = 480, 480
+    else:
+        target_h, target_w = 832, 480
     for resolution in bucket_config[closet_ratio][0]:
         if ori_height * ori_weight >= resolution[0] * resolution[1]:
             target_h, target_w = resolution
@@ -255,6 +300,10 @@ class WanAudioRunner(WanRunner):
     def __init__(self, config):
         super().__init__(config)
 
+    def init_scheduler(self):
+        scheduler = EulerSchedulerTimestepFix(self.config)
+        self.model.set_scheduler(scheduler)
+
     def load_audio_models(self):
         ##音频特征提取器
         self.audio_preprocess = AutoFeatureExtractor.from_pretrained(self.config["model_path"], subfolder="audio_encoder")
@@ -438,7 +487,14 @@ class WanAudioRunner(WanRunner):
 
             elif res_frame_num > 5 and idx == interval_num - 1:  # 最后一段可能不够81帧
                 prev_frames = torch.zeros((1, 3, max_num_frames, tgt_h, tgt_w), device=device)
-                prev_frames[:, :, :prev_frame_length] = gen_video_list[-1][:, :, -prev_frame_length:]
+                last_frames = gen_video_list[-1][:, :, -prev_frame_length:].clone().to(device)
+
+                last_frames = last_frames.cpu().detach().numpy()
+                last_frames = add_noise_to_frames(last_frames)
+                last_frames = add_mask_to_frames(last_frames, mask_rate=0.1)  # mask 0.10
+                last_frames = torch.from_numpy(last_frames).to(dtype=dtype, device=device)
+
+                prev_frames[:, :, :prev_frame_length] = last_frames
                 prev_latents = self.vae_encoder.encode(prev_frames.to(vae_dtype), self.config)[0].to(dtype)
                 prev_len = prev_token_length
                 audio_start, audio_end = get_audio_range(idx * max_num_frames - idx * prev_frame_length, expected_frames, fps=target_fps, audio_sr=audio_sr)
@@ -449,7 +505,14 @@ class WanAudioRunner(WanRunner):
 
             else:  # 中间段满81帧带pre_latens
                 prev_frames = torch.zeros((1, 3, max_num_frames, tgt_h, tgt_w), device=device)
-                prev_frames[:, :, :prev_frame_length] = gen_video_list[-1][:, :, -prev_frame_length:]
+                last_frames = gen_video_list[-1][:, :, -prev_frame_length:].clone().to(device)
+
+                last_frames = last_frames.cpu().detach().numpy()
+                last_frames = add_noise_to_frames(last_frames)
+                last_frames = add_mask_to_frames(last_frames, mask_rate=0.1)  # mask 0.10
+                last_frames = torch.from_numpy(last_frames).to(dtype=dtype, device=device)
+
+                prev_frames[:, :, :prev_frame_length] = last_frames
                 prev_latents = self.vae_encoder.encode(prev_frames.to(vae_dtype), self.config)[0].to(dtype)
                 prev_len = prev_token_length
                 audio_start, audio_end = get_audio_range(idx * max_num_frames - idx * prev_frame_length, (idx + 1) * max_num_frames - idx * prev_frame_length, fps=target_fps, audio_sr=audio_sr)
@@ -494,13 +557,13 @@ class WanAudioRunner(WanRunner):
             start_audio_frame = 0 if idx == 0 else int((prev_frame_length + 1) * audio_sr / target_fps)
 
             if res_frame_num > 5 and idx == interval_num - 1:
-                gen_video_list.append(gen_video[:, :, start_frame:res_frame_num])
+                gen_video_list.append(gen_video[:, :, start_frame:res_frame_num].cpu())
                 cut_audio_list.append(audio_array[start_audio_frame:useful_length])
             elif expected_frames < max_num_frames and useful_length != -1:
-                gen_video_list.append(gen_video[:, :, start_frame:expected_frames])
+                gen_video_list.append(gen_video[:, :, start_frame:expected_frames].cpu())
                 cut_audio_list.append(audio_array[start_audio_frame:useful_length])
             else:
-                gen_video_list.append(gen_video[:, :, start_frame:])
+                gen_video_list.append(gen_video[:, :, start_frame:].cpu())
                 cut_audio_list.append(audio_array[start_audio_frame:])
 
         gen_lvideo = torch.cat(gen_video_list, dim=2).float()

lightx2v/models/schedulers/wan/audio/audio.py

+import os
+import gc
+import math
+import numpy as np
+import torch
+from typing import List, Optional, Tuple, Union
+from lightx2v.utils.envs import *
+
+from diffusers.configuration_utils import register_to_config
+from torch import Tensor
+from .utils import unsqueeze_to_ndim
+from diffusers import (
+    FlowMatchEulerDiscreteScheduler as FlowMatchEulerDiscreteSchedulerBase,  # pyright: ignore
+)
+
+
+def get_timesteps(num_steps, max_steps: int = 1000):
+    return np.linspace(max_steps, 0, num_steps + 1, dtype=np.float32)
+
+
+def timestep_shift(timesteps, shift: float = 1.0):
+    return shift * timesteps / (1 + (shift - 1) * timesteps)
+
+
+class FlowMatchEulerDiscreteScheduler(FlowMatchEulerDiscreteSchedulerBase):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.init_noise_sigma = 1.0
+
+    def add_noise(self, x0: Tensor, noise: Tensor, timesteps: Tensor):
+        dtype = x0.dtype
+        device = x0.device
+        sigma = timesteps.to(device, torch.float32) / self.config.num_train_timesteps
+        sigma = unsqueeze_to_ndim(sigma, x0.ndim)
+        xt = x0.float() * (1 - sigma) + noise.float() * sigma
+        return xt.to(dtype)
+
+    def get_velocity(self, x0: Tensor, noise: Tensor, timesteps: Tensor | None = None):
+        return noise - x0
+
+    def velocity_loss_to_x_loss(self, v_loss: Tensor, timesteps: Tensor):
+        device = v_loss.device
+        sigma = timesteps.to(device, torch.float32) / self.config.num_train_timesteps
+        return v_loss.float() * (sigma**2)
+
+
+class EulerSchedulerTimestepFix(FlowMatchEulerDiscreteScheduler):
+    def __init__(self, config):
+        self.config = config
+        self.step_index = 0
+        self.latents = None
+        self.caching_records = [True] * config.infer_steps
+        self.flag_df = False
+        self.transformer_infer = None
+
+        self.device = torch.device("cuda")
+        self.infer_steps = self.config.infer_steps
+        self.target_video_length = self.config.target_video_length
+        self.sample_shift = self.config.sample_shift
+
+        self.num_train_timesteps = 1000
+
+        self.noise_pred = None
+
+    def step_pre(self, step_index):
+        self.step_index = step_index
+        if GET_DTYPE() == "BF16":
+            self.latents = self.latents.to(dtype=torch.bfloat16)
+
+    def set_shift(self, shift: float = 1.0):
+        self.sigmas = self.timesteps_ori / self.num_train_timesteps
+        self.sigmas = timestep_shift(self.sigmas, shift=shift)
+        self.timesteps = self.sigmas * self.num_train_timesteps
+
+    def set_timesteps(
+        self,
+        infer_steps: Union[int, None] = None,
+        device: Union[str, torch.device] = None,
+        sigmas: Optional[List[float]] = None,
+        mu: Optional[Union[float, None]] = None,
+        shift: Optional[Union[float, None]] = None,
+    ):
+        timesteps = get_timesteps(num_steps=infer_steps, max_steps=self.num_train_timesteps)
+        self.timesteps = torch.from_numpy(timesteps).to(dtype=torch.float32, device=device or self.device)
+        self.timesteps_ori = self.timesteps.clone()
+        self.set_shift(self.sample_shift)
+        self._step_index = None
+        self._begin_index = None
+
+    def prepare(self, image_encoder_output=None):
+        self.generator = torch.Generator(device=self.device)
+        self.generator.manual_seed(self.config.seed)
+
+        self.prepare_latents(self.config.target_shape, dtype=torch.float32)
+
+        if os.path.isfile(self.config.image_path):
+            self.seq_len = ((self.config.target_video_length - 1) // self.config.vae_stride[0] + 1) * self.config.lat_h * self.config.lat_w // (self.config.patch_size[1] * self.config.patch_size[2])
+        else:
+            self.seq_len = math.ceil((self.config.target_shape[2] * self.config.target_shape[3]) / (self.config.patch_size[1] * self.config.patch_size[2]) * self.config.target_shape[1])
+
+        self.model_outputs = [None] * self.solver_order
+        self.timestep_list = [None] * self.solver_order
+        self.last_sample = None
+
+        self.set_timesteps(infer_steps=self.infer_steps, device=self.device, shift=self.sample_shift)
+
+    def prepare_latents(self, target_shape, dtype=torch.float32):
+        self.latents = (
+            torch.randn(
+                target_shape[0],
+                target_shape[1],
+                target_shape[2],
+                target_shape[3],
+                dtype=dtype,
+                device=self.device,
+                generator=self.generator,
+            )
+            * self.init_noise_sigma
+        )
+
+    def step_post(self):
+        model_output = self.noise_pred.to(torch.float32)
+        timestep = self.timesteps[self.step_index]
+        sample = self.latents.to(torch.float32)
+
+        if self.step_index is None:
+            self._init_step_index(timestep)
+        sample = sample.to(torch.float32)  # pyright: ignore
+        sigma = unsqueeze_to_ndim(self.sigmas[self.step_index], sample.ndim)
+        sigma_next = unsqueeze_to_ndim(self.sigmas[self.step_index + 1], sample.ndim)
+        # x0 = sample - model_output * sigma
+        x_t_next = sample + (sigma_next - sigma) * model_output
+        self._step_index += 1
+        return x_t_next
+
+    def reset(self):
+        self.model_outputs = [None] * self.solver_order
+        self.timestep_list = [None] * self.solver_order
+        self.last_sample = None
+        self.noise_pred = None
+        self.this_order = None
+        self.lower_order_nums = 0
+        self.prepare_latents(self.config.target_shape, dtype=torch.float32)
+        gc.collect()
+        torch.cuda.empty_cache()

lightx2v/models/schedulers/wan/audio/scheduler.py

+import os
+import gc
+import math
+import numpy as np
+import torch
+from typing import List, Optional, Tuple, Union
+from lightx2v.utils.envs import *
+from lightx2v.models.schedulers.scheduler import BaseScheduler
+from loguru import logger
+
+from diffusers.configuration_utils import register_to_config
+from torch import Tensor
+from diffusers import (
+    FlowMatchEulerDiscreteScheduler as FlowMatchEulerDiscreteSchedulerBase,  # pyright: ignore
+)
+
+
+def unsqueeze_to_ndim(in_tensor: Tensor, tgt_n_dim: int):
+    if in_tensor.ndim > tgt_n_dim:
+        warnings.warn(f"the given tensor of shape {in_tensor.shape} is expected to unsqueeze to {tgt_n_dim}, the original tensor will be returned")
+        return in_tensor
+    if in_tensor.ndim < tgt_n_dim:
+        in_tensor = in_tensor[(...,) + (None,) * (tgt_n_dim - in_tensor.ndim)]
+    return in_tensor
+
+
+class EulerSchedulerTimestepFix(BaseScheduler):
+    def __init__(self, config, **kwargs):
+        # super().__init__(**kwargs)
+        self.init_noise_sigma = 1.0
+        self.config = config
+        self.latents = None
+        self.caching_records = [True] * config.infer_steps
+        self.flag_df = False
+        self.transformer_infer = None
+        self.solver_order = 2
+        self.device = torch.device("cuda")
+        self.infer_steps = self.config.infer_steps
+        self.target_video_length = self.config.target_video_length
+        self.sample_shift = self.config.sample_shift
+        self.shift = 1
+        self.num_train_timesteps = 1000
+        self.step_index = None
+        self.noise_pred = None
+
+        self._step_index = None
+        self._begin_index = None
+
+    def step_pre(self, step_index):
+        self.step_index = step_index
+        if GET_DTYPE() == "BF16":
+            self.latents = self.latents.to(dtype=torch.bfloat16)
+
+    def set_timesteps(
+        self,
+        infer_steps: Union[int, None] = None,
+        device: Union[str, torch.device] = None,
+        sigmas: Optional[List[float]] = None,
+        mu: Optional[Union[float, None]] = None,
+        shift: Optional[Union[float, None]] = None,
+    ):
+        sigmas = np.linspace(self.sigma_max, self.sigma_min, infer_steps + 1).copy()[:-1]
+
+        if shift is None:
+            shift = self.shift
+        sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)
+
+        sigma_last = 0
+
+        timesteps = sigmas * self.num_train_timesteps
+        sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32)
+
+        self.sigmas = torch.from_numpy(sigmas)
+        self.timesteps = torch.from_numpy(timesteps).to(device=device, dtype=torch.int64)
+
+        assert len(self.timesteps) == self.infer_steps
+        self.model_outputs = [
+            None,
+        ] * self.solver_order
+        self.lower_order_nums = 0
+        self.last_sample = None
+        self._begin_index = None
+        self.sigmas = self.sigmas.to("cpu")
+
+    def prepare(self, image_encoder_output=None):
+        self.generator = torch.Generator(device=self.device)
+        self.generator.manual_seed(self.config.seed)
+
+        self.prepare_latents(self.config.target_shape, dtype=torch.float32)
+
+        if self.config.task in ["t2v"]:
+            self.seq_len = math.ceil((self.config.target_shape[2] * self.config.target_shape[3]) / (self.config.patch_size[1] * self.config.patch_size[2]) * self.config.target_shape[1])
+        elif self.config.task in ["i2v"]:
+            self.seq_len = ((self.config.target_video_length - 1) // self.config.vae_stride[0] + 1) * self.config.lat_h * self.config.lat_w // (self.config.patch_size[1] * self.config.patch_size[2])
+
+        alphas = np.linspace(1, 1 / self.num_train_timesteps, self.num_train_timesteps)[::-1].copy()
+        sigmas = 1.0 - alphas
+        sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32)
+
+        sigmas = self.shift * sigmas / (1 + (self.shift - 1) * sigmas)
+
+        self.sigmas = sigmas
+        self.timesteps = sigmas * self.num_train_timesteps
+
+        self.model_outputs = [None] * self.solver_order
+        self.timestep_list = [None] * self.solver_order
+        self.last_sample = None
+
+        self.sigmas = self.sigmas.to("cpu")
+        self.sigma_min = self.sigmas[-1].item()
+        self.sigma_max = self.sigmas[0].item()
+
+        self.set_timesteps(self.infer_steps, device=self.device, shift=self.sample_shift)
+
+    def prepare_latents(self, target_shape, dtype=torch.float32):
+        self.latents = (
+            torch.randn(
+                target_shape[0],
+                target_shape[1],
+                target_shape[2],
+                target_shape[3],
+                dtype=dtype,
+                device=self.device,
+                generator=self.generator,
+            )
+            * self.init_noise_sigma
+        )
+
+    def step_post(self):
+        model_output = self.noise_pred.to(torch.float32)
+        sample = self.latents.to(torch.float32)
+
+        sample = sample.to(torch.float32)
+        sigma = unsqueeze_to_ndim(self.sigmas[self.step_index], sample.ndim).to(sample.device, sample.dtype)
+        sigma_next = unsqueeze_to_ndim(self.sigmas[self.step_index + 1], sample.ndim).to(sample.device, sample.dtype)
+        # x0 = sample - model_output * sigma
+        x_t_next = sample + (sigma_next - sigma) * model_output
+
+        self.latents = x_t_next
+
+    def reset(self):
+        self.model_outputs = [None]
+        self.timestep_list = [None]
+        self.last_sample = None
+        self.noise_pred = None
+        self.this_order = None
+        self.lower_order_nums = 0
+        self.prepare_latents(self.config.target_shape, dtype=torch.float32)
+        gc.collect()
+        torch.cuda.empty_cache()

scripts/wan/run_wan_i2v_audio.sh

@@ -4,6 +4,7 @@
 lightx2v_path="/mnt/Text2Video/wangshankun/lightx2v"
 model_path="/mnt/Text2Video/wangshankun/HF_Cache/Wan2.1-R2V-Audio-14B-720P/"
 #lora_path="/mnt/Text2Video/wuzhuguanyu/Wan21_I2V_14B_lightx2v_cfg_step_distill_lora_rank64.safetensors"
+#lora_path="/mnt/aigc/qiuzesong/Distill/DMD2/0716lightx2v/LightX2V/tools/extract/wan_r2v_V2_14B_lora_ran32.safetensors"
 # check section
 if [ -z "${CUDA_VISIBLE_DEVICES}" ]; then
     cuda_devices=0

tools/extract/convert_vigen_to_x2v_lora.py

@@ -5,12 +5,13 @@
 ###  ViGen-DiT Project Url: https://github.com/yl-1993/ViGen-DiT
 ###
 import torch
-from safetensors.torch import save_file
 import sys
 import os
+from safetensors.torch import save_file
+from safetensors.torch import load_file
 
 if len(sys.argv) != 3:
-    print("用法: python convert_lora.py <输入文件.pt> <输出文件.safetensors>")
+    print("用法: python convert_lora.py <输入文件> <输出文件.safetensors>")
     sys.exit(1)
 
 ckpt_path = sys.argv[1]
@@ -20,7 +21,10 @@ if not os.path.exists(ckpt_path):
     print(f"❌ 输入文件不存在: {ckpt_path}")
     sys.exit(1)
 
-state_dict = torch.load(ckpt_path, map_location="cpu")
+if ckpt_path.endswith(".safetensors"):
+    state_dict = load_file(ckpt_path)
+else:
+    state_dict = torch.load(ckpt_path, map_location="cpu")
 
 if "state_dict" in state_dict:
     state_dict = state_dict["state_dict"]