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Pre-compute VAE latents and text embeddings for Flux training.
Removes VAE/CLIP/T5 from GPU during training, saving ~10GB VRAM per GPU.
"""
import argparse
import io
from pathlib import Path
import torch
import webdataset as wds
from PIL import Image
from torchvision import transforms
from tqdm import tqdm
def get_transform(resolution=1024):
return transforms.Compose([
transforms.Resize(resolution, interpolation=transforms.InterpolationMode.LANCZOS),
transforms.CenterCrop(resolution),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
])
def main():
parser = argparse.ArgumentParser(description="Pre-compute embeddings for Flux training")
parser.add_argument("--model-name", default="black-forest-labs/FLUX.1-schnell")
parser.add_argument("--data-dir", type=Path, default=Path("/data0/datasets/processed/flux_train/shards"))
parser.add_argument("--output-dir", type=Path, default=Path("/data0/datasets/processed/flux_train/embeddings"))
parser.add_argument("--resolution", type=int, default=1024)
parser.add_argument("--batch-size", type=int, default=8)
parser.add_argument("--cache-dir", default="/data0/models")
parser.add_argument("--device", default="cuda:0")
args = parser.parse_args()
args.output_dir.mkdir(parents=True, exist_ok=True)
device = torch.device(args.device)
transform = get_transform(args.resolution)
# Load pipeline components
print("Loading Flux pipeline components...")
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained(
args.model_name,
torch_dtype=torch.bfloat16,
cache_dir=args.cache_dir,
)
vae = pipe.vae.to(device).eval()
text_encoder = pipe.text_encoder.to(device).eval()
text_encoder_2 = pipe.text_encoder_2.to(device).eval()
tokenizer = pipe.tokenizer
tokenizer_2 = pipe.tokenizer_2
vae_shift = vae.config.shift_factor
vae_scale = vae.config.scaling_factor
# Find tar shards
tar_files = sorted(args.data_dir.glob("*.tar"))
if not tar_files:
raise ValueError(f"No tar files found in {args.data_dir}")
print(f"Found {len(tar_files)} shards")
def decode_sample(sample):
try:
img = sample["jpg"]
if isinstance(img, bytes):
img = Image.open(io.BytesIO(img)).convert("RGB")
caption = sample.get("txt", b"")
if isinstance(caption, bytes):
caption = caption.decode("utf-8")
return {"image": transform(img), "caption": caption, "key": sample["__key__"]}
except:
return None
dataset = (
wds.WebDataset([str(f) for f in tar_files])
.decode("pil")
.map(decode_sample)
.select(lambda x: x is not None)
)
dataloader = torch.utils.data.DataLoader(
dataset, batch_size=None, num_workers=4, pin_memory=True
)
# Process in batches
batch_images = []
batch_captions = []
batch_keys = []
sample_idx = 0
shard_idx = 0
shard_data = []
samples_per_shard = 1000
print(f"Pre-computing embeddings (batch_size={args.batch_size})...")
def save_shard(shard_data, shard_idx):
shard_path = args.output_dir / f"shard-{shard_idx:06d}.pt"
torch.save(shard_data, shard_path)
return shard_idx + 1
def process_batch(images, captions, keys):
imgs = torch.stack(images).to(device, dtype=torch.bfloat16)
with torch.no_grad():
latents = vae.encode(imgs).latent_dist.sample()
latents = (latents - vae_shift) * vae_scale
# Pack latents: (B, C, H, W) -> (B, H/2*W/2, C*4)
b, c, h, w = latents.shape
packed = latents.view(b, c, h // 2, 2, w // 2, 2)
packed = packed.permute(0, 2, 4, 1, 3, 5).reshape(b, (h // 2) * (w // 2), c * 4)
# Text embeddings
text_ids = tokenizer(
captions, padding="max_length", max_length=77,
truncation=True, return_tensors="pt"
).input_ids.to(device)
pooled = text_encoder(text_ids, output_hidden_states=False).pooler_output
text_ids_2 = tokenizer_2(
captions, padding="max_length", max_length=256,
truncation=True, return_tensors="pt"
).input_ids.to(device)
hidden_states = text_encoder_2(text_ids_2)[0]
# Latent image IDs
latent_h, latent_w = h // 2, w // 2
img_ids = torch.zeros(latent_h, latent_w, 3, device=device)
img_ids[..., 1] = torch.arange(latent_h)[:, None].float()
img_ids[..., 2] = torch.arange(latent_w)[None, :].float()
img_ids = img_ids.reshape(latent_h * latent_w, 3)
# Text IDs
txt_ids = torch.zeros(hidden_states.shape[1], 3, device=device)
results = []
for i in range(b):
results.append({
"key": keys[i],
"packed_latents": packed[i].cpu(),
"pooled_prompt_embeds": pooled[i].cpu(),
"encoder_hidden_states": hidden_states[i].cpu(),
"img_ids": img_ids.cpu(),
"txt_ids": txt_ids.cpu(),
})
return results
total_processed = 0
for sample in dataloader:
batch_images.append(sample["image"])
batch_captions.append(sample["caption"])
batch_keys.append(sample["key"])
if len(batch_images) >= args.batch_size:
results = process_batch(batch_images, batch_captions, batch_keys)
shard_data.extend(results)
total_processed += len(results)
if len(shard_data) >= samples_per_shard:
shard_idx = save_shard(shard_data, shard_idx)
print(f" Saved shard {shard_idx - 1} ({total_processed} samples total)")
shard_data = []
batch_images = []
batch_captions = []
batch_keys = []
# Process remaining
if batch_images:
results = process_batch(batch_images, batch_captions, batch_keys)
shard_data.extend(results)
total_processed += len(results)
if shard_data:
shard_idx = save_shard(shard_data, shard_idx)
print(f" Saved shard {shard_idx - 1} ({total_processed} samples total)")
print(f"\nDone! {total_processed} samples saved to {args.output_dir} ({shard_idx} shards)")
if __name__ == "__main__":
main()
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