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feat: enhance WanVideoTeaCache with new coefficients and optional use_ret_steps parameter

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This commit is contained in:
GACLove
2025-06-10 14:37:02 +08:00
parent 874f47a5aa
commit eb09e217aa
1 changed files with 96 additions and 120 deletions
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nodes.py
+96 -120
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@@ -75,8 +75,8 @@ class WanVideoTeaCache:
[
"i2v-14B-720p",
"i2v-14B-480p",
"1.3B",
"14B",
"t2v-1.3B",
"t2v-14B",
"disabled",
],
{
@@ -84,6 +84,7 @@ class WanVideoTeaCache:
"tooltip": "Use coefficients for TeaCache. 'i2v-14B-720p' will use the default coefficients, 'disabled' will disable coefficients.",
},
),
"use_ret_steps": ("BOOLEAN", {"default": False}),
},
"optional": {
"mode": (
@@ -105,23 +106,47 @@ class WanVideoTeaCache:
def process(
self,
rel_l1_thresh,
start_step,
end_step,
cache_device,
coefficients,
rel_l1_thresh: float,
start_percent: float,
end_percent: float,
cache_device: str,
coefficients: str,
use_ret_steps: bool,
mode="e",
):
if cache_device == "main_device":
teacache_device = comfy_mm.get_torch_device()
else:
teacache_device = comfy_mm.unet_offload_device()
# use_ret_steps = True is [0]
# use_ret_steps = False is [1]
coeff_values_map = {
"i2v-14B-480p": [
[2.57151496e05, -3.54229917e04, 1.40286849e03, -1.35890334e01, 1.32517977e-01],
[-3.02331670e02, 2.23948934e02, -5.25463970e01, 5.87348440e00, -2.01973289e-01],
],
"i2v-14B-720p": [
[8.10705460e03, 2.13393892e03, -3.72934672e02, 1.66203073e01, -4.17769401e-02],
[-114.36346466, 65.26524496, -18.82220707, 4.91518089, -0.23412683],
],
"t2v-1.3B": [
[-5.21862437e04, 9.23041404e03, -5.28275948e02, 1.36987616e01, -4.99875664e-02],
[2.39676752e03, -1.31110545e03, 2.01331979e02, -8.29855975e00, 1.37887774e-01],
],
"t2v-14B": [
[-3.03318725e05, 4.90537029e04, -2.65530556e03, 5.87365115e01, -3.15583525e-01],
[-5784.54975374, 5449.50911966, -1811.16591783, 256.27178429, -13.02252404],
],
}
teacache_args = {
"rel_l1_thresh": rel_l1_thresh,
"start_step": start_step,
"end_step": end_step,
"start_percent": start_percent,
"end_percent": end_percent,
"cache_device": teacache_device,
"coefficients": coefficients,
"coefficients": coeff_values_map[coefficients],
"use_ret_steps": use_ret_steps,
"mode": mode,
}
return (teacache_args,)
@@ -134,9 +159,7 @@ class Lightx2vWanVideoModelDir:
"required": {
"model_dir": (
"STRING",
{
"default": "/mnt/aigc/users/lijiaqi2/wan_model/Wan2.1-I2V-14B-480P"
},
{"default": "/mnt/aigc/users/lijiaqi2/wan_model/Wan2.1-I2V-14B-480P"},
)
}
}
@@ -194,22 +217,17 @@ class Lightx2vWanVideoT5EncoderLoader:
tokenizer_path = model_dir / "google" / "umt5-xxl"
else:
model_name = Path(model_name)
assert model_name.exists(), (
f"T5 model path {model_name} does not exist. "
"Please provide a valid model path or set model_dir."
)
assert model_name.exists(), f"T5 model path {model_name} does not exist. Please provide a valid model path or set model_dir."
tokenizer_path = model_name.parent / "google" / "umt5-xxl"
assert tokenizer_path.exists(), (
f"Tokenizer path {tokenizer_path} does not exist. "
"Please provide a valid tokenizer path or set model_dir."
)
assert tokenizer_path.exists(), f"Tokenizer path {tokenizer_path} does not exist. Please provide a valid tokenizer path or set model_dir."
# Resolve device
if device == "cuda":
device = comfy_mm.get_torch_device()
cpu_offload = False
else:
device = torch.device("cpu")
cpu_offload = True
# Load the T5 encoder
t5_encoder = T5EncoderModel(
text_len=512, # Default text length for T5
@@ -218,6 +236,7 @@ class Lightx2vWanVideoT5EncoderLoader:
checkpoint_path=model_name,
tokenizer_path=tokenizer_path,
shard_fn=None,
cpu_offload=cpu_offload,
)
return (t5_encoder,)
@@ -300,27 +319,24 @@ class Lightx2vWanVideoVaeLoader:
model_name = model_dir / model_name
model_name = Path(model_name)
assert model_name.exists(), (
f"VAE model path {model_name} does not exist. "
"Please provide a valid model path or set model_dir."
)
assert model_name.exists(), f"VAE model path {model_name} does not exist. Please provide a valid model path or set model_dir."
# Resolve device
if device == "cuda":
device = comfy_mm.get_torch_device()
init_device = comfy_mm.get_torch_device()
else:
device = torch.device("cpu")
init_device = torch.device("cpu")
# Load the VAE
vae = WanVAE(
z_dim=16,
vae_pth=str(model_name),
dtype=dtype,
device=device, # type:ignore
device=init_device, # type:ignore
parallel=parallel,
)
return (vae,)
vae_result = {"vae_cls": vae, "device": device}
return (vae_result,)
class Lightx2vWanVideoVaeDecoder:
@@ -339,7 +355,8 @@ class Lightx2vWanVideoVaeDecoder:
CATEGORY = "LightX2V"
def decode_latent(self, wan_vae, latent):
config = EasyDict({"cpu_offload": False})
wan_vae_instance = wan_vae["vae_cls"]
config = EasyDict({"cpu_offload": True if wan_vae["device"] == "cpu" else False})
# 获取潜在表示和生成器
latents = latent["samples"]
@@ -348,7 +365,8 @@ class Lightx2vWanVideoVaeDecoder:
# 使用VAE解码潜在表示
with torch.no_grad():
# 解码得到视频帧
decoded_images = wan_vae.decode(latents, generator=generator, config=config)
with ProfilingContext("*decoded images*"):
decoded_images = wan_vae_instance.decode(latents, generator=generator, config=config)
# 将像素值从 [-1, 1] 归一化到 [0, 1]
images = (decoded_images + 1) / 2
@@ -374,9 +392,7 @@ class Lightx2vWanVideoClipVisionEncoderLoader:
"required": {
"model_name": (
"STRING",
{
"default": "models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"
},
{"default": "models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"},
),
"tokenizer_path": (
"STRING",
@@ -395,9 +411,7 @@ class Lightx2vWanVideoClipVisionEncoderLoader:
FUNCTION = "load_clip_vision_encoder"
CATEGORY = "LightX2V"
def load_clip_vision_encoder(
self, model_name, tokenizer_path, precision, device, model_dir=None
):
def load_clip_vision_encoder(self, model_name, tokenizer_path, precision, device, model_dir=None):
# Map precision to torch dtype
dtype_map = {"fp16": torch.float16, "fp32": torch.float32}
dtype = dtype_map[precision]
@@ -406,14 +420,8 @@ class Lightx2vWanVideoClipVisionEncoderLoader:
model_dir = Path(model_dir)
model_name = model_dir / model_name
tokenizer_path = model_dir / tokenizer_path
assert model_name.exists(), (
f"CLIP model path {model_name} does not exist. "
"Please provide a valid model path or set model_dir."
)
assert tokenizer_path.exists(), (
f"Tokenizer path {tokenizer_path} does not exist. "
"Please provide a valid model path or set model_dir."
)
assert model_name.exists(), f"CLIP model path {model_name} does not exist. Please provide a valid model path or set model_dir."
assert tokenizer_path.exists(), f"Tokenizer path {tokenizer_path} does not exist. Please provide a valid model path or set model_dir."
# Resolve device
if device == "cuda":
@@ -426,7 +434,9 @@ class Lightx2vWanVideoClipVisionEncoderLoader:
dtype=dtype,
device=device,
checkpoint_path=model_name,
tokenizer_path=tokenizer_path,
clip_quantized=False,
clip_quantized_ckpt=None,
quant_scheme=None,
)
return (clip_vision_encoder,)
@@ -480,7 +490,7 @@ class Lightx2vWanVideoImageEncoder:
def encode_image(
self,
vae: WanVAE,
vae: dict[str, WanVAE | str],
image,
clip_vision_encoder: ClipVisionModel,
height,
@@ -488,10 +498,10 @@ class Lightx2vWanVideoImageEncoder:
num_frames,
):
# 创建配置对象
vae_instance = vae["vae_cls"]
config = EasyDict(
{
"cpu_offload": False,
"cpu_offload": True if vae["device"] == "cpu" else False,
"target_height": height,
"target_width": width,
"target_video_length": num_frames,
@@ -508,28 +518,15 @@ class Lightx2vWanVideoImageEncoder:
img = img.sub_(0.5).div_(0.5) # 归一化到 [-1, 1]
# 使用CLIP视觉编码器编码图像
clip_encoder_out = (
clip_vision_encoder.visual([img[:, None, :, :]], config)
.squeeze(0)
.to(torch.bfloat16)
)
with ProfilingContext("*clip encoder*"):
clip_encoder_out = clip_vision_encoder.visual([img[:, None, :, :]], config).squeeze(0).to(torch.bfloat16)
# 计算宽高比和尺寸
h, w = img.shape[1:]
aspect_ratio = h / w
max_area = config.target_height * config.target_width
lat_h = round(
np.sqrt(max_area * aspect_ratio)
// config.vae_stride[1]
// config.patch_size[1]
* config.patch_size[1]
)
lat_w = round(
np.sqrt(max_area / aspect_ratio)
// config.vae_stride[2]
// config.patch_size[2]
* config.patch_size[2]
)
lat_h = round(np.sqrt(max_area * aspect_ratio) // config.vae_stride[1] // config.patch_size[1] * config.patch_size[1])
lat_w = round(np.sqrt(max_area / aspect_ratio) // config.vae_stride[2] // config.patch_size[2] * config.patch_size[2])
# XXX: trick
config.lat_h = lat_h
@@ -538,29 +535,24 @@ class Lightx2vWanVideoImageEncoder:
h = lat_h * config.vae_stride[1]
w = lat_w * config.vae_stride[2]
msk = torch.ones(
1, config.target_video_length, lat_h, lat_w, device=torch.device("cuda")
)
msk = torch.ones(1, config.target_video_length, lat_h, lat_w, device=torch.device("cuda"))
msk[:, 1:] = 0
msk = torch.concat(
[torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1
)
msk = torch.concat([torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1)
msk = msk.view(1, msk.shape[1] // 4, 4, lat_h, lat_w)
msk = msk.transpose(1, 2)[0]
vae_encode_out = vae.encode(
[
torch.concat(
[
torch.nn.functional.interpolate(
img[None].cpu(), size=(h, w), mode="bicubic"
).transpose(0, 1),
torch.zeros(3, config.target_video_length - 1, h, w),
],
dim=1,
).cuda()
],
config,
)[0]
with ProfilingContext("*vae encoder*"):
vae_encode_out: torch.Tensor = vae_instance.encode(
[
torch.concat(
[
torch.nn.functional.interpolate(img[None].cpu(), size=(h, w), mode="bicubic").transpose(0, 1),
torch.zeros(3, config.target_video_length - 1, h, w),
],
dim=1,
).cuda()
], # type: ignore
config,
)[0]
# TODO(xxx): hard code
vae_encode_out = torch.concat([msk, vae_encode_out]).to(torch.bfloat16)
@@ -664,9 +656,7 @@ class Lightx2vWanVideoModelLoader:
),
"model_dir": (
"STRING",
{
"default": "/mnt/aigc/users/lijiaqi2/wan_model/Wan2.1-I2V-14B-480P"
},
{"default": "/mnt/aigc/users/lijiaqi2/wan_model/Wan2.1-I2V-14B-480P"},
),
},
}
@@ -708,10 +698,7 @@ class Lightx2vWanVideoModelLoader:
model_name = Path(model_dir) / model_name
assert model_name, "Model path must be provided."
assert model_name.exists(), (
f"Model path {model_name} does not exist. "
"Please provide a valid model path or set model_dir."
)
assert model_name.exists(), f"Model path {model_name} does not exist. Please provide a valid model path or set model_dir."
if model_name.is_dir():
config_json_path = model_name / "config.json"
@@ -728,6 +715,7 @@ class Lightx2vWanVideoModelLoader:
feature_caching = "Tea" if teacache_args is not None else "NoCaching"
teacache_thresh = teacache_args["rel_l1_thresh"] if teacache_args else 0.26
use_ret_steps = teacache_args["use_ret_steps"] if teacache_args else False
# 创建配置字典
config = {
@@ -740,12 +728,9 @@ class Lightx2vWanVideoModelLoader:
"patch_size": (1, 2, 2),
"feature_caching": feature_caching, # ["NoCaching", "TaylorSeer", "Tea"]
"teacache_thresh": teacache_thresh,
"use_ret_steps": False,
"use_ret_steps": use_ret_steps,
"use_bfloat16": dtype == torch.bfloat16,
"mm_config": {
"mm_type": mm_type,
"weight_auto_quant": False if mm_type == "Default" else True,
},
"mm_config": {},
"model_path": model_name,
"task": model_type,
"model_cls": "wan2.1",
@@ -753,6 +738,7 @@ class Lightx2vWanVideoModelLoader:
"attention_type": attention_type,
"lora_path": lora_path if lora_path and lora_path.strip() else None,
"strength_model": lora_strength,
"offload_granularity": "block",
}
# merge model dir config.json and config dict
config.update(**config_json)
@@ -764,9 +750,7 @@ class Lightx2vWanVideoModelLoader:
# 如果指定了LoRA路径，应用LoRA
if lora_path and os.path.exists(lora_path):
logging.info(
f"Applying LoRA from {lora_path} with strength {lora_strength}"
)
logging.info(f"Applying LoRA from {lora_path} with strength {lora_strength}")
lora_wrapper = WanLoraWrapper(model)
lora_name = lora_wrapper.load_lora(lora_path)
lora_wrapper.apply_lora(lora_name, lora_strength)
@@ -791,7 +775,7 @@ class Lightx2vWanVideoSampler:
"FLOAT",
{"default": 5, "min": 1, "max": 20.0, "step": 0.1},
),
"seed": ("INT", {"default": 42}),
"seed": ("INT", {"default": 42, "min": 0, "max": 0xFFFFFFFFFFFFFFFF, "step": 1}),
}
}
@@ -826,9 +810,7 @@ class Lightx2vWanVideoSampler:
# text result
vae_encode_out = image_embeddings.get("vae_encode_out", None)
if model_config.task == "i2v" and (
clip_encoder_out is None or vae_encode_out is None
):
if model_config.task == "i2v" and (clip_encoder_out is None or vae_encode_out is None):
raise ValueError("clip_encoder_out must be provided for i2v task")
model_config.infer_steps = steps
@@ -837,7 +819,6 @@ class Lightx2vWanVideoSampler:
model_config.seed = seed
model_config.enable_cfg = False if math.isclose(cfg_scale, 1.0) else True
model_config.offload_granularity = "block"
# wan_runner.set_target_shape
num_channels_latents = model_config.get("num_channels_latents", 16)
@@ -845,8 +826,7 @@ class Lightx2vWanVideoSampler:
if model_config.task == "i2v":
model_config.target_shape = (
num_channels_latents,
(model_config.target_video_length - 1) // model_config.vae_stride[0]
+ 1,
(model_config.target_video_length - 1) // model_config.vae_stride[0] + 1,
model_config.lat_h,
model_config.lat_w,
)
@@ -882,24 +862,20 @@ class Lightx2vWanVideoSampler:
# Run sampling
progress = ProgressBar(steps)
for step_index in tqdm(
range(scheduler.infer_steps), desc="inference", unit="step"
):
with ProfilingContext("scheduler.step_pre"):
scheduler.step_pre(step_index=step_index)
for step_index in tqdm(range(scheduler.infer_steps), desc="inference", unit="step"):
scheduler.step_pre(step_index=step_index)
with ProfilingContext("model.infer"):
wan_model.infer(inputs)
with ProfilingContext("scheduler.step_post"):
scheduler.step_post()
scheduler.step_post()
progress.update(1)
latents, generator = scheduler.latents, scheduler.generator
scheduler.clear()
del inputs, wan_model, text_embeddings, image_embeddings
del inputs, scheduler, text_embeddings, image_embeddings
torch.cuda.empty_cache()
return ({"samples": scheduler.latents, "generator": scheduler.generator},)
return ({"samples": latents, "generator": generator},)
# Register the nodes