File size: 11,240 Bytes
f0a4e91 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 | import os
import sys
import numpy as np
import torch
from diffusers import FlowMatchEulerDiscreteScheduler
from omegaconf import OmegaConf
from PIL import Image
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)))]
for project_root in project_roots:
sys.path.insert(0, project_root) if project_root not in sys.path else None
from videox_fun.dist import set_multi_gpus_devices, shard_model
from videox_fun.models import (AutoencoderKLWan, AutoencoderKLWan3_8,
FlashHeadTransformer3DModel, FlashHeadAudioEncoder)
from videox_fun.pipeline import FlashHeadPipeline
from videox_fun.utils.fm_solvers import FlowDPMSolverMultistepScheduler
from videox_fun.utils.fm_solvers_unipc import FlowUniPCMultistepScheduler
from videox_fun.utils import (register_auto_device_hook,
safe_enable_group_offload)
from videox_fun.utils.fp8_optimization import (convert_model_weight_to_float8,
convert_weight_dtype_wrapper,
replace_parameters_by_name)
from videox_fun.utils.lora_utils import merge_lora, unmerge_lora
from videox_fun.utils.utils import (filter_kwargs, get_image_latent, get_image,
get_video_to_video_latent,
merge_video_audio, save_videos_grid)
# 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].
# model_full_load means that the entire model will be moved to the GPU.
#
# model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
# and the transformer model has been quantized to float8, which can save more GPU memory.
#
# model_cpu_offload means that the entire model will be moved to the CPU after use, which can save some GPU memory.
#
# model_cpu_offload_and_qfloat8 indicates that the entire model will be moved to the CPU after use,
# and the transformer model has been quantized to float8, which can save more GPU memory.
#
# model_group_offload transfers internal layer groups between CPU/CUDA,
# balancing memory efficiency and speed between full-module and leaf-level offloading methods.
#
# sequential_cpu_offload means that each layer of the model will be moved to the CPU after use,
# resulting in slower speeds but saving a large amount of GPU memory.
GPU_memory_mode = "model_full_load"
# Multi GPUs config
ulysses_degree = 1
ring_degree = 1
# Use FSDP to save more GPU memory in multi gpus.
fsdp_dit = False
# Compile will give a speedup in fixed resolution and need a little GPU memory.
# The compile_dit is not compatible with sequential_cpu_offload.
compile_dit = False
# Config and model path
config_path = "config/wan2.1/wan_civitai.yaml"
# model path
# Please Download https://modelscope.cn/models/AI-ModelScope/wav2vec2-base-960h/summary
model_name = "models/Diffusion_Transformer/SoulX-FlashHead-1_3B"
model_name_audio = "models/Diffusion_Transformer/wav2vec2-base-960h"
# Choose the sampler in "Flow", "Flow_Unipc", "Flow_DPM++"
sampler_name = "Flow"
shift = 5.0
stochastic_sampling = True
# Load pretrained model if need
transformer_path = None
vae_path = None
lora_path = None
# Other params
sample_size = [512, 512]
segment_frame_length = 33
fps = 25
# Use torch.float16 if GPU does not support torch.bfloat16
# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
weight_dtype = torch.bfloat16
# The path of the reference image
ref_image = "asset/9.png"
# The path of the audio
audio_path = "asset/talk.wav"
# Audio guidance scale (FlashHead does not use text encoder, only audio conditioning)
audio_guide_scale = 1.0
seed = 42
num_inference_steps = 4
lora_weight = 0.55
save_path = "samples/flashhead-videos"
# FlashHead specific parameters
max_frames_num = 500
color_correction_strength = 1.0
use_apg = False
apg_momentum = 0.5
apg_norm_threshold = 1.0
audio_encode_mode = "stream"
device = set_multi_gpus_devices(ulysses_degree, ring_degree)
config = OmegaConf.load(config_path)
transformer = FlashHeadTransformer3DModel.from_pretrained(
os.path.join(model_name, "Model_Pro", config['transformer_additional_kwargs'].get('transformer_subpath', 'transformer')),
transformer_additional_kwargs=OmegaConf.to_container(config['transformer_additional_kwargs']),
low_cpu_mem_usage=True,
torch_dtype=weight_dtype,
)
if transformer_path is not None:
print(f"From checkpoint: {transformer_path}")
if transformer_path.endswith("safetensors"):
from safetensors.torch import load_file
state_dict = load_file(transformer_path)
else:
state_dict = torch.load(transformer_path, map_location="cpu")
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)}")
# Get Vae
vae = AutoencoderKLWan.from_pretrained(
os.path.join(model_name, "VAE_Wan/Wan2.1_VAE.pth"),
additional_kwargs=OmegaConf.to_container(config['vae_kwargs']),
).to(weight_dtype)
if vae_path is not None:
print(f"From checkpoint: {vae_path}")
if vae_path.endswith("safetensors"):
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["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)}")
# Initialize FlashHead audio encoder for real-time audio encoding
# Uses Wav2Vec2Model (not Wav2Vec2ForCTC) matching original FlashHead implementation
audio_encoder = FlashHeadAudioEncoder(
model_name_audio, "cpu"
)
# Get Scheduler
Chosen_Scheduler = scheduler_dict = {
"Flow": FlowMatchEulerDiscreteScheduler,
"Flow_Unipc": FlowUniPCMultistepScheduler,
"Flow_DPM++": FlowDPMSolverMultistepScheduler,
}[sampler_name]
if sampler_name == "Flow_Unipc" or sampler_name == "Flow_DPM++":
config['scheduler_kwargs']['shift'] = 1
scheduler = Chosen_Scheduler(
**filter_kwargs(Chosen_Scheduler, OmegaConf.to_container(config['scheduler_kwargs']))
)
# Get Pipeline (FlashHead does not use text encoder or clip image encoder)
pipeline = FlashHeadPipeline(
transformer=transformer,
vae=vae,
scheduler=scheduler,
audio_encoder=audio_encoder,
)
if ulysses_degree > 1 or ring_degree > 1:
from functools import partial
transformer.enable_multi_gpus_inference()
if fsdp_dit:
shard_fn = partial(shard_model, device_id=device, param_dtype=weight_dtype)
pipeline.transformer = shard_fn(pipeline.transformer)
print("Add FSDP DIT")
if compile_dit:
for i in range(len(pipeline.transformer.blocks)):
pipeline.transformer.blocks[i] = torch.compile(pipeline.transformer.blocks[i])
print("Add Compile")
if GPU_memory_mode == "sequential_cpu_offload":
replace_parameters_by_name(transformer, ["modulation",], device=device)
transformer.freqs = transformer.freqs.to(device=device)
pipeline.enable_sequential_cpu_offload(device=device)
elif GPU_memory_mode == "model_group_offload":
register_auto_device_hook(pipeline.transformer)
safe_enable_group_offload(pipeline, onload_device=device, offload_device="cpu", offload_type="leaf_level", use_stream=True)
elif GPU_memory_mode == "model_cpu_offload_and_qfloat8":
convert_model_weight_to_float8(transformer, exclude_module_name=["modulation",], device=device)
convert_weight_dtype_wrapper(transformer, weight_dtype)
pipeline.enable_model_cpu_offload(device=device)
elif GPU_memory_mode == "model_cpu_offload":
pipeline.enable_model_cpu_offload(device=device)
elif GPU_memory_mode == "model_full_load_and_qfloat8":
convert_model_weight_to_float8(transformer, exclude_module_name=["modulation",], device=device)
convert_weight_dtype_wrapper(transformer, weight_dtype)
pipeline.to(device=device)
else:
pipeline.to(device=device)
generator = torch.Generator(device=device).manual_seed(seed)
if lora_path is not None:
pipeline = merge_lora(pipeline, lora_path, lora_weight, device=device, dtype=weight_dtype)
with torch.no_grad():
# For FlashHead, (segment_frame_length - 1) must be divisible by 4
segment_frame_length = (segment_frame_length - 1) // vae.config.temporal_compression_ratio * vae.config.temporal_compression_ratio + 1 if segment_frame_length != 1 else 1
latent_frames = (segment_frame_length - 1) // vae.config.temporal_compression_ratio + 1
# Prepare ref_image latent for FlashHead (no clip_image needed)
ref_image = get_image_latent(ref_image, sample_size=sample_size)
sample = pipeline(
segment_frame_length = segment_frame_length,
height = sample_size[0],
width = sample_size[1],
generator = generator,
audio_guide_scale = audio_guide_scale,
num_inference_steps = num_inference_steps,
ref_image = ref_image,
audio_path = audio_path,
audio_encode_mode = audio_encode_mode,
shift = shift,
fps = fps,
max_frames_num = max_frames_num,
color_correction_strength = color_correction_strength,
use_apg = use_apg,
apg_momentum = apg_momentum,
apg_norm_threshold = apg_norm_threshold,
stochastic_sampling = stochastic_sampling,
).videos
if lora_path is not None:
pipeline = unmerge_lora(pipeline, lora_path, lora_weight, device=device, dtype=weight_dtype)
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 sample.size()[2] == 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)
merge_video_audio(video_path=video_path, audio_path=audio_path)
if ulysses_degree * ring_degree > 1:
import torch.distributed as dist
if dist.get_rank() == 0:
save_results()
else:
save_results()
|