diff --git a/examples/wan2.1/predict_i2v_speed.py b/examples/wan2.1/predict_i2v_speed.py index a241794..d47fba3 100644 --- a/examples/wan2.1/predict_i2v_speed.py +++ b/examples/wan2.1/predict_i2v_speed.py @@ -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() diff --git a/examples/wan2.1/predict_i2v_speed.sh b/examples/wan2.1/predict_i2v_speed.sh index a05625d..19d6760 100644 --- a/examples/wan2.1/predict_i2v_speed.sh +++ b/examples/wan2.1/predict_i2v_speed.sh @@ -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 \