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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 | from utils.wan_wrapper import WanDiffusionWrapper, WanTextEncoder, WanVAEWrapper
from utils.scheduler import FlowMatchScheduler
from utils.distributed import launch_distributed_job
import torch.distributed as dist
from tqdm import tqdm
import argparse
import torch
import math
import os
from utils.dataset import LatentLMDBDataset
def init_model(device):
model = WanDiffusionWrapper(is_causal=True).to(device).to(torch.float32)
model.model.num_frame_per_block = 3 # !!
encoder = WanTextEncoder().to(device).to(torch.float32)
scheduler = FlowMatchScheduler(shift=5.0, sigma_min=0.0, extra_one_step=True)
scheduler.set_timesteps(num_inference_steps=48, denoising_strength=1.0)
scheduler.sigmas = scheduler.sigmas.to(device)
sample_neg_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'
unconditional_dict = encoder(
text_prompts=[sample_neg_prompt]
)
return model, encoder, scheduler, unconditional_dict
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--local_rank", type=int, default=-1)
parser.add_argument("--output_folder", type=str)
parser.add_argument("--rawdata_path", type=str)
parser.add_argument("--generator_ckpt", type=str)
parser.add_argument("--guidance_scale", type=float, default=6.0)
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)
state_dict = torch.load(args.generator_ckpt, map_location="cpu")
gen_sd = state_dict["generator"]
fixed = {}
for k, v in gen_sd.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
state_dict = fixed
model.model.load_state_dict(
state_dict, strict=True
)
dataset = LatentLMDBDataset(args.rawdata_path)
if global_rank == 0:
os.makedirs(args.output_folder, exist_ok=True)
total_steps = int(math.ceil(len(dataset) / dist.get_world_size()))
for index in tqdm(
range(total_steps), disable=(dist.get_rank() != 0),
):
prompt_index = index * dist.get_world_size() + dist.get_rank()
if prompt_index >= len(dataset):
continue
sample = dataset[prompt_index]
prompt = sample["prompts"]
clean_latent = sample["clean_latent"].to(device).unsqueeze(0)
conditional_dict = encoder(
text_prompts=prompt
)
latents = torch.randn(
[1, 21, 16, 60, 104], dtype=torch.float32, device=device
)
noisy_input = []
for progress_id, t in enumerate(tqdm(scheduler.timesteps, disable=(dist.get_rank() != 0))):
timestep = t * \
torch.ones([1, 21], device=device, dtype=torch.float32)
noisy_input.append(latents)
f_cond, x0_pred_cond = model(
latents, conditional_dict, timestep, clean_x = clean_latent
)
f_uncond, x0_pred_uncond = model(
latents, unconditional_dict, timestep, clean_x = clean_latent
)
flow_pred = f_uncond + args.guidance_scale * (
f_cond - f_uncond
)
latents = scheduler.step(
flow_pred.flatten(0, 1),
timestep.flatten(0, 1),
latents.flatten(0, 1)
).unflatten(dim=0, sizes=flow_pred.shape[:2])
noisy_input.append(latents)
noisy_input.append(clean_latent)
noisy_inputs = torch.stack(noisy_input, dim=1)
noisy_inputs = noisy_inputs[:, [0, 12, 24, 36, -2, -1]]
stored_data = noisy_inputs
torch.save(
{prompt: stored_data.cpu().detach()},
os.path.join(args.output_folder, f"{prompt_index:05d}.pt")
)
dist.barrier()
if __name__ == "__main__":
main()
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