300 lines
14 KiB
Python
300 lines
14 KiB
Python
import os
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import sys
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import numpy as np
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import torch
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from diffusers import FlowMatchEulerDiscreteScheduler
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from omegaconf import OmegaConf
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from PIL import Image
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from transformers import AutoTokenizer
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current_file_path = os.path.abspath(__file__)
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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)))]
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for project_root in project_roots:
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sys.path.insert(0, project_root) if project_root not in sys.path else None
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from videox_fun.dist import set_multi_gpus_devices, shard_model
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from videox_fun.models import (AutoencoderKLWan, AutoTokenizer,
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WanT5EncoderModel, WanTransformer3DModel)
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from videox_fun.models.cache_utils import get_teacache_coefficients
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from videox_fun.pipeline import WanFunPhantomPipeline
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from videox_fun.utils.fp8_optimization import (convert_model_weight_to_float8,
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convert_weight_dtype_wrapper,
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replace_parameters_by_name)
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from videox_fun.utils.lora_utils import merge_lora, unmerge_lora
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from videox_fun.utils.utils import (filter_kwargs, get_image_latent,
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save_videos_grid)
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from videox_fun.utils.fm_solvers import FlowDPMSolverMultistepScheduler
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from videox_fun.utils.fm_solvers_unipc import FlowUniPCMultistepScheduler
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# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
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# model_full_load means that the entire model will be moved to the GPU.
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#
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# model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
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# and the transformer model has been quantized to float8, which can save more GPU memory.
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#
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# model_cpu_offload means that the entire model will be moved to the CPU after use, which can save some GPU memory.
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#
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# model_cpu_offload_and_qfloat8 indicates that the entire model will be moved to the CPU after use,
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# and the transformer model has been quantized to float8, which can save more GPU memory.
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#
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# sequential_cpu_offload means that each layer of the model will be moved to the CPU after use,
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# resulting in slower speeds but saving a large amount of GPU memory.
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GPU_memory_mode = "sequential_cpu_offload"
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# Multi GPUs config
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# Please ensure that the product of ulysses_degree and ring_degree equals the number of GPUs used.
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# For example, if you are using 8 GPUs, you can set ulysses_degree = 2 and ring_degree = 4.
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# If you are using 1 GPU, you can set ulysses_degree = 1 and ring_degree = 1.
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ulysses_degree = 1
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ring_degree = 1
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# Use FSDP to save more GPU memory in multi gpus.
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fsdp_dit = False
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fsdp_text_encoder = True
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# Compile will give a speedup in fixed resolution and need a little GPU memory.
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# The compile_dit is not compatible with the fsdp_dit and sequential_cpu_offload.
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compile_dit = False
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# Support TeaCache.
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enable_teacache = True
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# Recommended to be set between 0.05 and 0.30. A larger threshold can cache more steps, speeding up the inference process,
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# but it may cause slight differences between the generated content and the original content.
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# # --------------------------------------------------------------------------------------------------- #
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# | Model Name | threshold | Model Name | threshold | Model Name | threshold |
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# | Wan2.1-T2V-1.3B | 0.05~0.10 | Wan2.1-T2V-14B | 0.10~0.15 | Wan2.1-I2V-14B-720P | 0.20~0.30 |
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# | Wan2.1-I2V-14B-480P | 0.20~0.25 | Wan2.1-Fun-*-1.3B-* | 0.05~0.10 | Wan2.1-Fun-*-14B-* | 0.20~0.30 |
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# # --------------------------------------------------------------------------------------------------- #
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teacache_threshold = 0.10
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# The number of steps to skip TeaCache at the beginning of the inference process, which can
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# reduce the impact of TeaCache on generated video quality.
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num_skip_start_steps = 5
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# Whether to offload TeaCache tensors to cpu to save a little bit of GPU memory.
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teacache_offload = False
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# Skip some cfg steps in inference
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# Recommended to be set between 0.00 and 0.25
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cfg_skip_ratio = 0
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# Riflex config
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enable_riflex = False
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# Index of intrinsic frequency
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riflex_k = 6
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# Config and model path
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config_path = "config/wan2.1/wan_civitai.yaml"
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# model path
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model_name = "models/Diffusion_Transformer/Wan2.1-T2V-1.3B"
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# Choose the sampler in "Flow", "Flow_Unipc", "Flow_DPM++"
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sampler_name = "Flow_Unipc"
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# [NOTE]: Noise schedule shift parameter. Affects temporal dynamics.
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# Used when the sampler is in "Flow_Unipc", "Flow_DPM++".
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# If you want to generate a 480p video, it is recommended to set the shift value to 3.0.
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# If you want to generate a 720p video, it is recommended to set the shift value to 5.0.
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shift = 3
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# Load pretrained model if need
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transformer_path = "models/Personalized_Model/Phantom-Wan-1.3B.safetensors"
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vae_path = None
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lora_path = None
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# Other params
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sample_size = [480, 832]
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video_length = 81
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fps = 16
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# Use torch.float16 if GPU does not support torch.bfloat16
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# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
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weight_dtype = torch.bfloat16
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subject_ref_images = ["asset/ref_1.png", "asset/ref_2.png"]
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# 使用更长的neg prompt如"模糊，突变，变形，失真，画面暗，文本字幕，画面固定，连环画，漫画，线稿，没有主体。"，可以增加稳定性
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# 在neg prompt中添加"安静，固定"等词语可以增加动态性。
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prompt = "暖阳漫过草地，扎着双马尾、头戴绿色蝴蝶结、身穿浅绿色连衣裙的小女孩蹲在盛开的雏菊旁。她身旁一只棕白相间的狗狗吐着舌头，毛茸茸尾巴欢快摇晃。小女孩笑着举起黄红配色、带有蓝色按钮的玩具相机，将和狗狗的欢乐瞬间定格。"
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negative_prompt = "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"
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# Using longer neg prompt such as "Blurring, mutation, deformation, distortion, dark and solid, comics, text subtitles, line art." can increase stability
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# Adding words such as "quiet, solid" to the neg prompt can increase dynamism.
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# prompt = "A young woman with beautiful, clear eyes and blonde hair stands in the forest, wearing a white dress and a crown. Her expression is serene, reminiscent of a movie star, with fair and youthful skin. Her brown long hair flows in the wind. The video quality is very high, with a clear view. High quality, masterpiece, best quality, high resolution, ultra-fine, fantastical."
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# negative_prompt = "Twisted body, limb deformities, text captions, comic, static, ugly, error, messy code."
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guidance_scale = 6.0
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seed = 43
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num_inference_steps = 50
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lora_weight = 0.55
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save_path = "samples/wan-videos-phantom"
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device = set_multi_gpus_devices(ulysses_degree, ring_degree)
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config = OmegaConf.load(config_path)
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transformer = WanTransformer3DModel.from_pretrained(
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os.path.join(model_name, config['transformer_additional_kwargs'].get('transformer_subpath', 'transformer')),
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transformer_additional_kwargs=OmegaConf.to_container(config['transformer_additional_kwargs']),
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low_cpu_mem_usage=True,
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torch_dtype=weight_dtype,
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)
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if transformer_path is not None:
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print(f"From checkpoint: {transformer_path}")
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if transformer_path.endswith("safetensors"):
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from safetensors.torch import load_file, safe_open
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state_dict = load_file(transformer_path)
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else:
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state_dict = torch.load(transformer_path, map_location="cpu")
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state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict
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m, u = transformer.load_state_dict(state_dict, strict=False)
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print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")
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# Get Vae
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vae = AutoencoderKLWan.from_pretrained(
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os.path.join(model_name, config['vae_kwargs'].get('vae_subpath', 'vae')),
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additional_kwargs=OmegaConf.to_container(config['vae_kwargs']),
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).to(weight_dtype)
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if vae_path is not None:
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print(f"From checkpoint: {vae_path}")
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if vae_path.endswith("safetensors"):
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from safetensors.torch import load_file, safe_open
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state_dict = load_file(vae_path)
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else:
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state_dict = torch.load(vae_path, map_location="cpu")
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state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict
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m, u = vae.load_state_dict(state_dict, strict=False)
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print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")
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# Get Tokenizer
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tokenizer = AutoTokenizer.from_pretrained(
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os.path.join(model_name, config['text_encoder_kwargs'].get('tokenizer_subpath', 'tokenizer')),
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)
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# Get Text encoder
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text_encoder = WanT5EncoderModel.from_pretrained(
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os.path.join(model_name, config['text_encoder_kwargs'].get('text_encoder_subpath', 'text_encoder')),
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additional_kwargs=OmegaConf.to_container(config['text_encoder_kwargs']),
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low_cpu_mem_usage=True,
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torch_dtype=weight_dtype,
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)
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text_encoder = text_encoder.eval()
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# Get Scheduler
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Chosen_Scheduler = scheduler_dict = {
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"Flow": FlowMatchEulerDiscreteScheduler,
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"Flow_Unipc": FlowUniPCMultistepScheduler,
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"Flow_DPM++": FlowDPMSolverMultistepScheduler,
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}[sampler_name]
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if sampler_name == "Flow_Unipc" or sampler_name == "Flow_DPM++":
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config['scheduler_kwargs']['shift'] = 1
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scheduler = Chosen_Scheduler(
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**filter_kwargs(Chosen_Scheduler, OmegaConf.to_container(config['scheduler_kwargs']))
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)
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# Get Pipeline
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pipeline = WanFunPhantomPipeline(
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transformer=transformer,
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vae=vae,
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tokenizer=tokenizer,
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text_encoder=text_encoder,
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scheduler=scheduler
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)
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if ulysses_degree > 1 or ring_degree > 1:
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from functools import partial
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transformer.enable_multi_gpus_inference()
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if fsdp_dit:
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shard_fn = partial(shard_model, device_id=device, param_dtype=weight_dtype)
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pipeline.transformer = shard_fn(pipeline.transformer)
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print("Add FSDP DIT")
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if fsdp_text_encoder:
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shard_fn = partial(shard_model, device_id=device, param_dtype=weight_dtype)
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pipeline.text_encoder = shard_fn(pipeline.text_encoder)
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print("Add FSDP TEXT ENCODER")
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if compile_dit:
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for i in range(len(pipeline.transformer.blocks)):
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pipeline.transformer.blocks[i] = torch.compile(pipeline.transformer.blocks[i])
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print("Add Compile")
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if GPU_memory_mode == "sequential_cpu_offload":
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replace_parameters_by_name(transformer, ["modulation",], device=device)
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transformer.freqs = transformer.freqs.to(device=device)
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pipeline.enable_sequential_cpu_offload(device=device)
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elif GPU_memory_mode == "model_cpu_offload_and_qfloat8":
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convert_model_weight_to_float8(transformer, exclude_module_name=["modulation",], device=device)
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convert_weight_dtype_wrapper(transformer, weight_dtype)
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pipeline.enable_model_cpu_offload(device=device)
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elif GPU_memory_mode == "model_cpu_offload":
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pipeline.enable_model_cpu_offload(device=device)
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elif GPU_memory_mode == "model_full_load_and_qfloat8":
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convert_model_weight_to_float8(transformer, exclude_module_name=["modulation",], device=device)
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convert_weight_dtype_wrapper(transformer, weight_dtype)
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pipeline.to(device=device)
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else:
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pipeline.to(device=device)
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coefficients = get_teacache_coefficients(model_name) if enable_teacache else None
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if coefficients is not None:
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print(f"Enable TeaCache with threshold {teacache_threshold} and skip the first {num_skip_start_steps} steps.")
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pipeline.transformer.enable_teacache(
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coefficients, num_inference_steps, teacache_threshold, num_skip_start_steps=num_skip_start_steps, offload=teacache_offload
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)
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if cfg_skip_ratio is not None:
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print(f"Enable cfg_skip_ratio {cfg_skip_ratio}.")
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pipeline.transformer.enable_cfg_skip(cfg_skip_ratio, num_inference_steps)
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generator = torch.Generator(device=device).manual_seed(seed)
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if lora_path is not None:
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pipeline = merge_lora(pipeline, lora_path, lora_weight, device=device)
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with torch.no_grad():
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video_length = int((video_length - 1) // vae.config.temporal_compression_ratio * vae.config.temporal_compression_ratio) + 1 if video_length != 1 else 1
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latent_frames = (video_length - 1) // vae.config.temporal_compression_ratio + 1
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if enable_riflex:
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pipeline.transformer.enable_riflex(k = riflex_k, L_test = latent_frames)
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if subject_ref_images is not None:
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subject_ref_images = [get_image_latent(_subject_ref_image, sample_size=sample_size, padding=True) for _subject_ref_image in subject_ref_images]
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subject_ref_images = torch.cat(subject_ref_images, dim=2)
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sample = pipeline(
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prompt,
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num_frames = video_length,
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negative_prompt = negative_prompt,
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height = sample_size[0],
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width = sample_size[1],
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generator = generator,
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guidance_scale = guidance_scale,
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num_inference_steps = num_inference_steps,
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subject_ref_images = subject_ref_images,
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shift = shift,
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).videos
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if lora_path is not None:
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pipeline = unmerge_lora(pipeline, lora_path, lora_weight, device=device)
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def save_results():
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if not os.path.exists(save_path):
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os.makedirs(save_path, exist_ok=True)
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index = len([path for path in os.listdir(save_path)]) + 1
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prefix = str(index).zfill(8)
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if video_length == 1:
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video_path = os.path.join(save_path, prefix + ".png")
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image = sample[0, :, 0]
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image = image.transpose(0, 1).transpose(1, 2)
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image = (image * 255).numpy().astype(np.uint8)
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image = Image.fromarray(image)
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image.save(video_path)
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else:
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video_path = os.path.join(save_path, prefix + ".mp4")
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save_videos_grid(sample, video_path, fps=fps)
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if ulysses_degree * ring_degree > 1:
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import torch.distributed as dist
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if dist.get_rank() == 0:
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save_results()
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else:
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save_results() |