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ae8ade0 | 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 | import argparse
import math
import os
import torch
import torch.distributed as dist
from tqdm import tqdm
from utils.dataset import LatentLMDBDataset
from utils.distributed import launch_distributed_job
from utils.ode_generation import (
CausalODETrajectoryGenerator,
merge_cfg_prompt_embeds,
)
from utils.scheduler import FlowMatchScheduler
from utils.wan_wrapper import WanDiffusionWrapper, WanTextEncoder
DEFAULT_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"
)
DEFAULT_NUM_INFERENCE_STEPS = 48
DEFAULT_SCHEDULER_SHIFT = 5.0
DEFAULT_TARGET_NUM_FRAMES = 21
DEFAULT_TRAJECTORY_INDICES = [0, 12, 24, 36, -2, -1]
def normalize_generator_state_dict(state_dict: dict) -> dict:
if "generator" in state_dict:
state_dict = state_dict["generator"]
elif "generator_ema" in state_dict:
state_dict = state_dict["generator_ema"]
fixed = {}
for k, v in state_dict.items():
if k.startswith("model._fsdp_wrapped_module."):
k = k.replace("model._fsdp_wrapped_module.", "", 1)
if k.startswith("model."):
k = k.replace("model.", "", 1)
fixed[k] = v
return fixed
def init_model(
device,
num_frame_per_block: int,
scheduler_shift: float,
num_inference_steps: int,
negative_prompt: str,
):
model = WanDiffusionWrapper(is_causal=True).to(device).to(torch.float32)
model.model.num_frame_per_block = num_frame_per_block
encoder = WanTextEncoder().to(device).to(torch.float32)
scheduler = FlowMatchScheduler(
shift=scheduler_shift,
sigma_min=0.0,
extra_one_step=True,
)
scheduler.set_timesteps(
num_inference_steps=num_inference_steps,
denoising_strength=1.0,
)
scheduler.sigmas = scheduler.sigmas.to(device)
unconditional_dict = encoder(text_prompts=[negative_prompt])
return model, encoder, scheduler, unconditional_dict
def prepare_clean_latent(
sample: dict,
target_num_frames: int | None,
device,
) -> torch.Tensor:
clean_latent = sample["clean_latent"].to(device).unsqueeze(0)
if target_num_frames is None:
return clean_latent
if clean_latent.shape[1] < target_num_frames:
raise ValueError(
"clean_latent has fewer frames than requested: "
f"{clean_latent.shape[1]} < {target_num_frames}"
)
if clean_latent.shape[1] != target_num_frames:
clean_latent = clean_latent[:, :target_num_frames, ...]
return clean_latent.contiguous()
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--local_rank", type=int, default=-1)
parser.add_argument("--output_folder", type=str, required=True)
parser.add_argument("--rawdata_path", type=str, required=True)
parser.add_argument("--generator_ckpt", type=str, required=True)
parser.add_argument("--num_frames_per_chunk", type=int, required=True)
parser.add_argument("--guidance_scale", type=float, default=6.0)
parser.add_argument(
"--generation_mode",
type=str,
default="full",
choices=["full", "blockwise_kv"],
)
args = parser.parse_args()
launch_distributed_job()
global_rank = dist.get_rank()
device = torch.cuda.current_device()
torch.set_grad_enabled(False)
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
model, encoder, scheduler, unconditional_dict = init_model(
device=device,
num_frame_per_block=args.num_frame_per_block,
scheduler_shift=DEFAULT_SCHEDULER_SHIFT,
num_inference_steps=DEFAULT_NUM_INFERENCE_STEPS,
negative_prompt=DEFAULT_NEGATIVE_PROMPT,
)
state_dict = torch.load(args.generator_ckpt, map_location="cpu")
model.model.load_state_dict(
normalize_generator_state_dict(state_dict),
strict=True,
)
dataset = LatentLMDBDataset(
args.rawdata_path,
max_pair=int(1e8),
)
if global_rank == 0:
os.makedirs(args.output_folder, exist_ok=True)
trajectory_generator = CausalODETrajectoryGenerator(
model=model,
scheduler=scheduler,
num_frame_per_block=args.num_frame_per_block,
num_inference_steps=DEFAULT_NUM_INFERENCE_STEPS,
guidance_scale=args.guidance_scale,
)
total_steps = int(math.ceil(len(dataset) / dist.get_world_size()))
for index in tqdm(
range(total_steps), disable=(global_rank != 0),
):
prompt_index = index * dist.get_world_size() + global_rank
if prompt_index >= len(dataset):
continue
output_path = os.path.join(args.output_folder, f"{prompt_index:05d}.pt")
sample = dataset[prompt_index]
prompt = sample["prompts"]
clean_latent = prepare_clean_latent(
sample=sample,
target_num_frames=DEFAULT_TARGET_NUM_FRAMES,
device=device,
)
conditional_dict = encoder(text_prompts=[prompt])
paired_conditional_dict = merge_cfg_prompt_embeds(
conditional_dict=conditional_dict,
unconditional_dict=unconditional_dict,
)
initial_noise = torch.randn_like(clean_latent, dtype=torch.float32)
stored_data = trajectory_generator.generate(
clean_latent=clean_latent,
paired_conditional_dict=paired_conditional_dict,
trajectory_indices=DEFAULT_TRAJECTORY_INDICES,
generation_mode=args.generation_mode,
initial_noise=initial_noise,
)
torch.save(
{prompt: stored_data.cpu().detach()},
output_path,
)
dist.barrier()
if __name__ == "__main__":
main()
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