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Update i2v speed

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This commit is contained in:
bubbliiiing
2025-06-02 16:24:49 +00:00
parent 4b0b009fd3
commit 61fb59833f
2 changed files with 174 additions and 182 deletions
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examples/wan2.1/predict_i2v_speed.py
+165 -173
View File
@@ -6,111 +6,84 @@ import torch
from diffusers import FlowMatchEulerDiscreteScheduler
from omegaconf import OmegaConf
from PIL import Image
from transformers import AutoTokenizer
# 添加项目根目录到 sys.path
# 添加项目根目录到系统路径
current_file_path = os.path.abspath(__file__)
project_roots = [
os.path.dirname(current_file_path),
os.path.dirname(os.path.dirname(current_file_path)),
os.path.dirname(os.path.dirname(os.path.dirname(current_file_path)))
]
project_roots = [os.path.dirname(current_file_path), os.path.dirname(os.path.dirname(current_file_path)), os.path.dirname(os.path.dirname(os.path.dirname(current_file_path)))]
for project_root in project_roots:
if project_root not in sys.path:
sys.path.insert(0, project_root)
sys.path.insert(0, project_root) if project_root not in sys.path else None
# 导入模块
from videox_fun.dist import set_multi_gpus_devices, shard_model
from videox_fun.models import (AutoencoderKLWan, WanT5EncoderModel, AutoTokenizer, WanTransformer3DModel)
from videox_fun.models import (AutoencoderKLWan, AutoTokenizer, CLIPModel,
WanT5EncoderModel, WanTransformer3DModel)
from videox_fun.models.cache_utils import get_teacache_coefficients
from videox_fun.pipeline import WanPipeline
from videox_fun.pipeline import WanI2VPipeline
from videox_fun.utils.fp8_optimization import (convert_model_weight_to_float8, replace_parameters_by_name,
convert_weight_dtype_wrapper)
from videox_fun.utils.lora_utils import merge_lora, unmerge_lora
from videox_fun.utils.utils import (filter_kwargs, save_videos_grid)
from videox_fun.utils.utils import (filter_kwargs, get_image_to_video_latent,
save_videos_grid)
from videox_fun.utils.fm_solvers import FlowDPMSolverMultistepScheduler
from videox_fun.utils.fm_solvers_unipc import FlowUniPCMultistepScheduler
def parse_args():
parser = argparse.ArgumentParser(description="Video Generation with Wan2.1-Fun")
# GPU Memory Optimization
parser.add_argument("--GPU_memory_mode", type=str, default="sequential_cpu_offload",
choices=["model_full_load", "model_full_load_and_qfloat8", "model_cpu_offload",
"model_cpu_offload_and_qfloat8", "sequential_cpu_offload"],
help="GPU memory optimization mode.")
parser.add_argument("--ulysses_degree", type=int, default=1,
help="Ulysses parallelism degree.")
parser.add_argument("--ring_degree", type=int, default=1,
help="Ring parallelism degree.")
parser.add_argument("--fsdp_dit", action="store_true",
help="Use FSDP for transformer to save GPU memory.")
parser.add_argument("--fsdp_text_encoder", action="store_true",
help="Use FSDP for text encoder to save GPU memory.")
parser.add_argument("--compile_dit", action="store_true",
help="Compile transformer for fixed resolution speedup.")
# TeaCache
parser.add_argument("--enable_teacache", action="store_true",
help="Enable TeaCache optimization.")
parser.add_argument("--teacache_threshold", type=float, default=0.10,
help="TeaCache threshold for step caching.")
parser.add_argument("--num_skip_start_steps", type=int, default=5,
help="Number of steps to skip TeaCache at inference start.")
parser.add_argument("--teacache_offload", action="store_true",
help="Offload TeaCache tensors to CPU.")
# CFG Skip
parser.add_argument("--cfg_skip_ratio", type=float, default=0.0,
help="CFG skip ratio for inference.")
# Riflex
parser.add_argument("--enable_riflex", action="store_true",
help="Enable Riflex frequency optimization.")
parser.add_argument("--riflex_k", type=int, default=6,
help="Intrinsic frequency index for Riflex.")
# Model Paths
parser.add_argument("--config_path", type=str, required=True,
help="Path to model config file.")
parser.add_argument("--model_name", type=str, required=True,
help="Path to model directory.")
parser.add_argument("--transformer_path", type=str, default=None,
help="Path to pre-trained transformer checkpoint.")
parser.add_argument("--vae_path", type=str, default=None,
help="Path to pre-trained VAE checkpoint.")
parser.add_argument("--lora_path", type=str, default=None,
help="Path to LoRA weights.")
# Generation Parameters
parser.add_argument("--sample_size", nargs=2, type=int, default=[480, 832],
help="Sample size [height, width].")
parser.add_argument("--video_length", type=int, default=81,
help="Number of frames in the video.")
parser.add_argument("--fps", type=int, default=16,
help="Frames per second for output video.")
parser.add_argument("--weight_dtype", type=str, default="bfloat16",
choices=["float16", "bfloat16"],
help="Weight data type (float16 or bfloat16).")
parser.add_argument("--prompt", type=str, required=True,
help="Text prompt for video generation.")
parser.add_argument("--negative_prompt", type=str, default="",
help="Negative prompt for video generation.")
parser.add_argument("--guidance_scale", type=float, default=6.0,
help="Classifier-free guidance scale.")
parser.add_argument("--seed", type=int, default=43,
help="Random seed for reproducibility.")
parser.add_argument("--num_inference_steps", type=int, default=50,
help="Number of inference steps.")
parser.add_argument("--lora_weight", type=float, default=0.55,
help="LoRA weight scaling factor.")
parser.add_argument("--save_path", type=str, default="samples/wan-videos-t2v",
help="Directory to save generated videos.")
# 解析命令行参数
parser = argparse.ArgumentParser()
# 基础模型与推理参数
parser.add_argument("--GPU_memory_mode", type=str, default="sequential_cpu_offload")
parser.add_argument("--ulysses_degree", type=int, default=1)
parser.add_argument("--ring_degree", type=int, default=1)
parser.add_argument("--fsdp_dit", action="store_true")
parser.add_argument("--fsdp_text_encoder", action="store_true")
parser.add_argument("--compile_dit", action="store_true")
# TeaCache 参数
parser.add_argument("--enable_teacache", action="store_true")
parser.add_argument("--teacache_threshold", type=float, default=0.10)
parser.add_argument("--num_skip_start_steps", type=int, default=5)
parser.add_argument("--teacache_offload", action="store_true")
# CFG Skip 参数
parser.add_argument("--cfg_skip_ratio", type=float, default=0.0)
# Riflex 参数
parser.add_argument("--enable_riflex", action="store_true")
parser.add_argument("--riflex_k", type=int, default=6)
# 模型路径和配置
parser.add_argument("--config_path", type=str, default="config/wan2.1/wan_civitai.yaml")
parser.add_argument("--model_name", type=str, default="models/Diffusion_Transformer/Wan2.1-I2V-14B-480P")
parser.add_argument("--transformer_path", type=str, default=None)
parser.add_argument("--vae_path", type=str, default=None)
parser.add_argument("--lora_path", type=str, default=None)
parser.add_argument("--sample_size", nargs='+', type=int, default=[480, 832])
parser.add_argument("--video_length", type=int, default=81)
parser.add_argument("--fps", type=int, default=16)
parser.add_argument("--weight_dtype", type=str, default="bfloat16")
# 输入图像相关
parser.add_argument("--validation_image_start", type=str, default="asset/1.png")
parser.add_argument("--validation_image_end", type=str, default=None)
# 推理参数
parser.add_argument("--prompt", type=str, default="一只棕色的狗摇着头，坐在舒适房间里的浅色沙发上。在狗的后面，架子上有一幅镶框的画，周围是粉红色的花朵。房间里柔和温暖的灯光营造出舒适的氛围。")
parser.add_argument("--negative_prompt", type=str, default="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走")
parser.add_argument("--guidance_scale", type=float, default=6.0)
parser.add_argument("--seed", type=int, default=43)
parser.add_argument("--num_inference_steps", type=int, default=50)
parser.add_argument("--lora_weight", type=float, default=0.55)
parser.add_argument("--save_path", type=str, default="samples/wan-videos-i2v")
# 采样器设置
parser.add_argument("--sampler_name", type=str, choices=["Flow", "Flow_Unipc", "Flow_DPM++"], default="Flow_Unipc")
parser.add_argument("--shift", type=float, default=3.0)
return parser.parse_args()
args = parse_args()
# 将 argparse 参数映射到原有变量
# 获取参数
GPU_memory_mode = args.GPU_memory_mode
ulysses_degree = args.ulysses_degree
ring_degree = args.ring_degree
@@ -140,90 +113,97 @@ seed = args.seed
num_inference_steps = args.num_inference_steps
lora_weight = args.lora_weight
save_path = args.save_path
sampler_name = args.sampler_name
shift = args.shift
validation_image_start = args.validation_image_start
validation_image_end = args.validation_image_end
# 设备设置
device = set_multi_gpus_devices(ulysses_degree, ring_degree)
# 加载配置
config = OmegaConf.load(config_path)
# 加载 Transformer 模型
# 初始化模型组件
transformer = WanTransformer3DModel.from_pretrained(
os.path.join(model_name, config['transformer_additional_kwargs'].get('transformer_subpath', 'transformer')),
transformer_additional_kwargs=OmegaConf.to_container(config['transformer_additional_kwargs']),
low_cpu_mem_usage=not fsdp_dit,
torch_dtype=weight_dtype
low_cpu_mem_usage=True,
torch_dtype=weight_dtype,
)
# 加载 Transformer Checkpoint（如有）
if transformer_path is not None:
print(f"From checkpoint: {transformer_path}")
if transformer_path.endswith("safetensors"):
from safetensors.torch import load_file
from safetensors.torch import load_file, safe_open
state_dict = load_file(transformer_path)
else:
state_dict = torch.load(transformer_path, map_location="cpu")
state_dict = state_dict.get("state_dict", state_dict)
state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict
m, u = transformer.load_state_dict(state_dict, strict=False)
print(f"Missing keys: {len(m)}, Unexpected keys: {len(u)}")
print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")
# 加载 VAE
# 获取VAE
vae = AutoencoderKLWan.from_pretrained(
os.path.join(model_name, config['vae_kwargs'].get('vae_subpath', 'vae')),
additional_kwargs=OmegaConf.to_container(config['vae_kwargs']),
).to(weight_dtype)
# 加载 VAE Checkpoint（如有）
if vae_path is not None:
print(f"From checkpoint: {vae_path}")
if vae_path.endswith("safetensors"):
from safetensors.torch import load_file
from safetensors.torch import load_file, safe_open
state_dict = load_file(vae_path)
else:
state_dict = torch.load(vae_path, map_location="cpu")
state_dict = state_dict.get("state_dict", state_dict)
state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict
m, u = vae.load_state_dict(state_dict, strict=False)
print(f"Missing keys: {len(m)}, Unexpected keys: {len(u)}")
print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")
# 加载 Tokenizer
# 获取分词器
tokenizer = AutoTokenizer.from_pretrained(
os.path.join(model_name, config['text_encoder_kwargs'].get('tokenizer_subpath', 'tokenizer'))
os.path.join(model_name, config['text_encoder_kwargs'].get('tokenizer_subpath', 'tokenizer')),
)
# 加载 Text Encoder
# 获取文本编码器
text_encoder = WanT5EncoderModel.from_pretrained(
os.path.join(model_name, config['text_encoder_kwargs'].get('text_encoder_subpath', 'text_encoder')),
additional_kwargs=OmegaConf.to_container(config['text_encoder_kwargs']),
low_cpu_mem_usage=True,
torch_dtype=weight_dtype
torch_dtype=weight_dtype,
)
text_encoder = text_encoder.eval()
# 加载 Scheduler
scheduler_class = {
# 获取CLIP图像编码器
clip_image_encoder = CLIPModel.from_pretrained(
os.path.join(model_name, config['image_encoder_kwargs'].get('image_encoder_subpath', 'image_encoder')),
).to(weight_dtype)
clip_image_encoder = clip_image_encoder.eval()
# 获取调度器
scheduler_dict = {
"Flow": FlowMatchEulerDiscreteScheduler,
"Flow_Unipc": FlowUniPCMultistepScheduler,
"Flow_DPM++": FlowDPMSolverMultistepScheduler
}[args.sampler_name]
"Flow_DPM++": FlowDPMSolverMultistepScheduler,
}
Choosen_Scheduler = scheduler_dict[sampler_name]
if args.sampler_name in ["Flow_Unipc", "Flow_DPM++"]:
if sampler_name in ["Flow_Unipc", "Flow_DPM++"]:
config['scheduler_kwargs']['shift'] = 1
scheduler = scheduler_class(
**filter_kwargs(scheduler_class, OmegaConf.to_container(config['scheduler_kwargs']))
scheduler = Choosen_Scheduler(
**filter_kwargs(Choosen_Scheduler, OmegaConf.to_container(config['scheduler_kwargs']))
)
# 构建 Pipeline
pipeline = WanPipeline(
# 创建Pipeline
pipeline = WanI2VPipeline(
transformer=transformer,
vae=vae,
tokenizer=tokenizer,
text_encoder=text_encoder,
scheduler=scheduler
scheduler=scheduler,
clip_image_encoder=clip_image_encoder
)
# 多卡并行设置
# 分布式设置
if ulysses_degree > 1 or ring_degree > 1:
from functools import partial
transformer.enable_multi_gpus_inference()
@@ -260,68 +240,80 @@ elif GPU_memory_mode == "model_full_load_and_qfloat8":
else:
pipeline.to(device=device)
# TeaCache 配置
coefficients = get_teacache_coefficients(model_name) if enable_teacache else None
if coefficients is not None:
print(f"Enable TeaCache with threshold {teacache_threshold} and skip the first {num_skip_start_steps} steps.")
pipeline.transformer.enable_teacache(
coefficients, num_inference_steps, teacache_threshold,
num_skip_start_steps=num_skip_start_steps, offload=teacache_offload
)
for i in range(2):
# TeaCache配置
coefficients = get_teacache_coefficients(model_name) if enable_teacache else None
if coefficients is not None:
print(f"Enable TeaCache with threshold {teacache_threshold} and skip the first {num_skip_start_steps} steps.")
pipeline.transformer.enable_teacache(
coefficients, num_inference_steps, teacache_threshold, num_skip_start_steps=num_skip_start_steps, offload=teacache_offload
)
# CFG Skip 配置
if cfg_skip_ratio is not None:
print(f"Enable cfg_skip_ratio {cfg_skip_ratio}.")
pipeline.transformer.enable_cfg_skip(cfg_skip_ratio, num_inference_steps)
# CFG跳过配置
if cfg_skip_ratio is not None and cfg_skip_ratio > 0:
print(f"Enable cfg_skip_ratio {cfg_skip_ratio}.")
pipeline.transformer.enable_cfg_skip(cfg_skip_ratio, num_inference_steps)
# 随机种子
generator = torch.Generator(device=device).manual_seed(seed)
# 随机种子
generator = torch.Generator(device=device).manual_seed(seed)
# 加载 LoRA（如有）
if lora_path is not None:
pipeline = merge_lora(pipeline, lora_path, lora_weight, device=device)
# LoRA加载
if lora_path is not None:
pipeline = merge_lora(pipeline, lora_path, lora_weight, device=device)
# 执行推理
with torch.no_grad():
latent_frames = (video_length - 1) // vae.config.temporal_compression_ratio + 1
video_length = int((video_length - 1) // vae.config.temporal_compression_ratio * vae.config.temporal_compression_ratio) + 1 if video_length != 1 else 1
# 生成视频
with torch.no_grad():
video_length = int((video_length - 1) // vae.config.temporal_compression_ratio * vae.config.temporal_compression_ratio) + 1 if video_length != 1 else 1
latent_frames = (video_length - 1) // vae.config.temporal_compression_ratio + 1
if enable_riflex:
pipeline.transformer.enable_riflex(k=riflex_k, L_test=latent_frames)
if enable_riflex:
pipeline.transformer.enable_riflex(k=riflex_k, L_test=latent_frames)
sample = pipeline(
prompt,
num_frames=video_length,
negative_prompt=negative_prompt,
height=sample_size[0],
width=sample_size[1],
generator=generator,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
).videos
# 输入图像处理
input_video, input_video_mask, clip_image = get_image_to_video_latent(validation_image_start, validation_image_end, video_length=video_length, sample_size=sample_size)
# 卸载 LoRA（如有）
if lora_path is not None:
pipeline = unmerge_lora(pipeline, lora_path, lora_weight, device=device)
# 执行推理
sample = pipeline(
prompt,
num_frames=video_length,
negative_prompt=negative_prompt,
height=sample_size[0],
width=sample_size[1],
generator=generator,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
video=input_video,
mask_video=input_video_mask,
clip_image=clip_image,
shift=shift,
).videos
# 保存结果
def save_results():
if not os.path.exists(save_path):
os.makedirs(save_path, exist_ok=True)
index = len([path for path in os.listdir(save_path)]) + 1
prefix = str(index).zfill(8)
if video_length == 1:
video_path = os.path.join(save_path, f"{prefix}.png")
image = sample[0, :, 0].permute(1, 2, 0).cpu().numpy()
image = (image * 255).astype(np.uint8)
Image.fromarray(image).save(video_path)
# LoRA卸载
if lora_path is not None:
pipeline = unmerge_lora(pipeline, lora_path, lora_weight, device=device)
# 保存结果
def save_results():
if not os.path.exists(save_path):
os.makedirs(save_path, exist_ok=True)
index = len([path for path in os.listdir(save_path)]) + 1
prefix = str(index).zfill(8)
if video_length == 1:
video_path = os.path.join(save_path, prefix + ".png")
image = sample[0, :, 0]
image = image.transpose(0, 1).transpose(1, 2)
image = (image * 255).numpy().astype(np.uint8)
image = Image.fromarray(image)
image.save(video_path)
else:
video_path = os.path.join(save_path, prefix + ".mp4")
save_videos_grid(sample, video_path, fps=fps)
# 分布式保存
if ulysses_degree * ring_degree > 1:
import torch.distributed as dist
if dist.get_rank() == 0:
save_results()
else:
video_path = os.path.join(save_path, f"{prefix}.mp4")
save_videos_grid(sample, video_path, fps=fps)
if ulysses_degree * ring_degree > 1:
import torch.distributed as dist
if dist.get_rank() == 0:
save_results()
else:
save_results()
examples/wan2.1/predict_i2v_speed.sh
+9 -9
View File
@@ -3,50 +3,50 @@ export EXCEL_FILE="./speed.xlsx"
export DIT_EXCEL_COL=0 VAE_EXCEL_COL=1 TOTAL_EXCEL_COL=2
# 14B 720P
export DIT_EXCEL_ROW=9 VAE_EXCEL_ROW=9 TOTAL_EXCEL_ROW=9
export DIT_EXCEL_ROW=1 VAE_EXCEL_ROW=1 TOTAL_EXCEL_ROW=1
python examples/wan2.1/predict_i2v_speed.py --model_name="models/Diffusion_Transformer/Wan2.1-I2V-14B-720P" \
--GPU_memory_mode="model_full_load_and_qfloat8" --ulysses_degree=1 --ring_degree=1 --fsdp_text_encoder --compile_dit \
--GPU_memory_mode="model_full_load_and_qfloat8" --ulysses_degree=1 --ring_degree=1 --fsdp_text_encoder --fsdp_dit \
--enable_teacache --teacache_threshold=0.30 --num_skip_start_steps=2 --cfg_skip_ratio=0.25 --shift=5 \
--sample_size 720 1280 --num_inference_steps=40
export DIT_EXCEL_ROW=10 VAE_EXCEL_ROW=10 TOTAL_EXCEL_ROW=10
export DIT_EXCEL_ROW=2 VAE_EXCEL_ROW=2 TOTAL_EXCEL_ROW=2
torchrun --nproc-per-node=2 examples/wan2.1/predict_i2v_speed.py --model_name="models/Diffusion_Transformer/Wan2.1-I2V-14B-720P" \
--GPU_memory_mode="model_full_load_and_qfloat8" --ulysses_degree=2 --ring_degree=1 --fsdp_text_encoder --fsdp_dit \
--enable_teacache --teacache_threshold=0.30 --num_skip_start_steps=2 --cfg_skip_ratio=0.25 --shift=5 \
--sample_size 720 1280 --num_inference_steps=40
export DIT_EXCEL_ROW=11 VAE_EXCEL_ROW=11 TOTAL_EXCEL_ROW=11
export DIT_EXCEL_ROW=3 VAE_EXCEL_ROW=3 TOTAL_EXCEL_ROW=3
torchrun --nproc-per-node=4 examples/wan2.1/predict_i2v_speed.py --model_name="models/Diffusion_Transformer/Wan2.1-I2V-14B-720P" \
--GPU_memory_mode="model_full_load_and_qfloat8" --ulysses_degree=4 --ring_degree=1 --fsdp_text_encoder --fsdp_dit \
--enable_teacache --teacache_threshold=0.30 --num_skip_start_steps=2 --cfg_skip_ratio=0.25 --shift=5 \
--sample_size 720 1280 --num_inference_steps=40
export DIT_EXCEL_ROW=12 VAE_EXCEL_ROW=12 TOTAL_EXCEL_ROW=12
export DIT_EXCEL_ROW=4 VAE_EXCEL_ROW=4 TOTAL_EXCEL_ROW=4
torchrun --nproc-per-node=8 examples/wan2.1/predict_i2v_speed.py --model_name="models/Diffusion_Transformer/Wan2.1-I2V-14B-720P" \
--GPU_memory_mode="model_full_load_and_qfloat8" --ulysses_degree=8 --ring_degree=1 --fsdp_text_encoder --fsdp_dit \
--enable_teacache --teacache_threshold=0.30 --num_skip_start_steps=2 --cfg_skip_ratio=0.25 --shift=5 \
--sample_size 720 1280 --num_inference_steps=40
# 14B 480P
export DIT_EXCEL_ROW=13 VAE_EXCEL_ROW=13 TOTAL_EXCEL_ROW=13
export DIT_EXCEL_ROW=5 VAE_EXCEL_ROW=5 TOTAL_EXCEL_ROW=5
python examples/wan2.1/predict_i2v_speed.py --model_name="models/Diffusion_Transformer/Wan2.1-I2V-14B-720P" \
--GPU_memory_mode="model_full_load_and_qfloat8" --ulysses_degree=1 --ring_degree=1 --fsdp_text_encoder --compile_dit \
--enable_teacache --teacache_threshold=0.30 --num_skip_start_steps=2 --cfg_skip_ratio=0.25 --shift=3 \
--sample_size 480 832 --num_inference_steps=40
export DIT_EXCEL_ROW=14 VAE_EXCEL_ROW=14 TOTAL_EXCEL_ROW=14
export DIT_EXCEL_ROW=6 VAE_EXCEL_ROW=6 TOTAL_EXCEL_ROW=6
torchrun --nproc-per-node=2 examples/wan2.1/predict_i2v_speed.py --model_name="models/Diffusion_Transformer/Wan2.1-I2V-14B-720P" \
--GPU_memory_mode="model_full_load_and_qfloat8" --ulysses_degree=2 --ring_degree=1 --fsdp_text_encoder --fsdp_dit \
--enable_teacache --teacache_threshold=0.30 --num_skip_start_steps=2 --cfg_skip_ratio=0.25 --shift=3 \
--sample_size 480 832 --num_inference_steps=40
export DIT_EXCEL_ROW=15 VAE_EXCEL_ROW=15 TOTAL_EXCEL_ROW=15
export DIT_EXCEL_ROW=7 VAE_EXCEL_ROW=7 TOTAL_EXCEL_ROW=7
torchrun --nproc-per-node=4 examples/wan2.1/predict_i2v_speed.py --model_name="models/Diffusion_Transformer/Wan2.1-I2V-14B-720P" \
--GPU_memory_mode="model_full_load_and_qfloat8" --ulysses_degree=4 --ring_degree=1 --fsdp_text_encoder --fsdp_dit \
--enable_teacache --teacache_threshold=0.30 --num_skip_start_steps=2 --cfg_skip_ratio=0.25 --shift=3 \
--sample_size 480 832 --num_inference_steps=40
export DIT_EXCEL_ROW=16 VAE_EXCEL_ROW=16 TOTAL_EXCEL_ROW=16
export DIT_EXCEL_ROW=8 VAE_EXCEL_ROW=8 TOTAL_EXCEL_ROW=8
torchrun --nproc-per-node=8 examples/wan2.1/predict_i2v_speed.py --model_name="models/Diffusion_Transformer/Wan2.1-I2V-14B-720P" \
--GPU_memory_mode="model_full_load_and_qfloat8" --ulysses_degree=8 --ring_degree=1 --fsdp_text_encoder --fsdp_dit \
--enable_teacache --teacache_threshold=0.30 --num_skip_start_steps=2 --cfg_skip_ratio=0.25 --shift=3 \