File size: 8,678 Bytes
8447bf6 | 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 | """Model loading utilities for Z-Image components."""
import json
import os
from pathlib import Path
import sys
from typing import Optional, Union
from loguru import logger
from safetensors.torch import load_file
import torch
from transformers import AutoModel, AutoTokenizer
from config import (
DEFAULT_SCHEDULER_NUM_TRAIN_TIMESTEPS,
DEFAULT_SCHEDULER_SHIFT,
DEFAULT_SCHEDULER_USE_DYNAMIC_SHIFTING,
DEFAULT_TRANSFORMER_CAP_FEAT_DIM,
DEFAULT_TRANSFORMER_DIM,
DEFAULT_TRANSFORMER_F_PATCH_SIZE,
DEFAULT_TRANSFORMER_IN_CHANNELS,
DEFAULT_TRANSFORMER_N_HEADS,
DEFAULT_TRANSFORMER_N_KV_HEADS,
DEFAULT_TRANSFORMER_N_LAYERS,
DEFAULT_TRANSFORMER_N_REFINER_LAYERS,
DEFAULT_TRANSFORMER_NORM_EPS,
DEFAULT_TRANSFORMER_PATCH_SIZE,
DEFAULT_TRANSFORMER_QK_NORM,
DEFAULT_TRANSFORMER_T_SCALE,
DEFAULT_VAE_IN_CHANNELS,
DEFAULT_VAE_LATENT_CHANNELS,
DEFAULT_VAE_NORM_NUM_GROUPS,
DEFAULT_VAE_OUT_CHANNELS,
DEFAULT_VAE_SCALING_FACTOR,
ROPE_AXES_DIMS,
ROPE_AXES_LENS,
ROPE_THETA,
)
from zimage.autoencoder import AutoencoderKL as LocalAutoencoderKL
from zimage.scheduler import FlowMatchEulerDiscreteScheduler
DIFFUSERS_AVAILABLE = False
def load_config(config_path: str) -> dict:
with open(config_path, "r") as f:
return json.load(f)
def load_sharded_safetensors(weight_dir: Path, device: str = "cuda", dtype: Optional[torch.dtype] = None) -> dict:
"""Load sharded safetensors from a directory."""
weight_dir = Path(weight_dir)
index_files = list(weight_dir.glob("*.safetensors.index.json"))
state_dict = {}
if index_files:
# Load sharded weights
with open(index_files[0], "r") as f:
index = json.load(f)
weight_map = index.get("weight_map", {})
shard_files = set(weight_map.values())
for shard_file in shard_files:
shard_path = weight_dir / shard_file
shard_state = load_file(str(shard_path), device=str(device))
state_dict.update(shard_state)
else:
# Load single safetensors file
safetensors_files = list(weight_dir.glob("*.safetensors"))
if not safetensors_files:
raise FileNotFoundError(f"No safetensors files found in {weight_dir}")
state_dict = load_file(str(safetensors_files[0]), device=str(device))
# Cast to target dtype if specified
if dtype is not None:
state_dict = {k: v.to(dtype) if v.dtype != dtype else v for k, v in state_dict.items()}
return state_dict
def load_from_local_dir(
model_dir: Union[str, Path],
device: str = "cuda",
dtype: torch.dtype = torch.bfloat16,
verbose: bool = False,
compile: bool = False,
) -> dict:
"""
Load all Z-Image components from local directory.
Args:
model_dir: Path to model directory
device: Device to load models on
dtype: Data type for model weights
verbose: Whether to display loading logs
compile: Whether to compile transformer and vae with torch.compile
Returns:
Dictionary containing transformer, vae, text_encoder, tokenizer, and scheduler
"""
model_dir = Path(model_dir)
sys.path.insert(0, str(model_dir.parent.parent / "Z-Image" / "src"))
from zimage.transformer import ZImageTransformer2DModel
if verbose:
logger.info(f"Loading Z-Image from: {model_dir}")
# DiT
if verbose:
logger.info("Loading DiT...")
transformer_dir = model_dir / "transformer"
config = load_config(str(transformer_dir / "config.json"))
with torch.device("meta"):
transformer = ZImageTransformer2DModel(
all_patch_size=tuple(config.get("all_patch_size", DEFAULT_TRANSFORMER_PATCH_SIZE)),
all_f_patch_size=tuple(config.get("all_f_patch_size", DEFAULT_TRANSFORMER_F_PATCH_SIZE)),
in_channels=config.get("in_channels", DEFAULT_TRANSFORMER_IN_CHANNELS),
dim=config.get("dim", DEFAULT_TRANSFORMER_DIM),
n_layers=config.get("n_layers", DEFAULT_TRANSFORMER_N_LAYERS),
n_refiner_layers=config.get("n_refiner_layers", DEFAULT_TRANSFORMER_N_REFINER_LAYERS),
n_heads=config.get("n_heads", DEFAULT_TRANSFORMER_N_HEADS),
n_kv_heads=config.get("n_kv_heads", DEFAULT_TRANSFORMER_N_KV_HEADS),
norm_eps=config.get("norm_eps", DEFAULT_TRANSFORMER_NORM_EPS),
qk_norm=config.get("qk_norm", DEFAULT_TRANSFORMER_QK_NORM),
cap_feat_dim=config.get("cap_feat_dim", DEFAULT_TRANSFORMER_CAP_FEAT_DIM),
rope_theta=config.get("rope_theta", ROPE_THETA),
t_scale=config.get("t_scale", DEFAULT_TRANSFORMER_T_SCALE),
axes_dims=config.get("axes_dims", ROPE_AXES_DIMS),
axes_lens=config.get("axes_lens", ROPE_AXES_LENS),
).to(dtype)
# DiT (weights to CPU then move to GPU to optimize memory)
state_dict = load_sharded_safetensors(transformer_dir, device="cpu", dtype=dtype)
transformer.load_state_dict(state_dict, strict=False, assign=True)
del state_dict
if verbose:
logger.info("Moving DiT to GPU...")
transformer = transformer.to(device)
if torch.cuda.is_available():
torch.cuda.empty_cache()
transformer.eval()
# VAE
if verbose:
logger.info("Loading VAE...")
vae_dir = model_dir / "vae"
vae_config = load_config(str(vae_dir / "config.json"))
vae = LocalAutoencoderKL(
in_channels=vae_config.get("in_channels", DEFAULT_VAE_IN_CHANNELS),
out_channels=vae_config.get("out_channels", DEFAULT_VAE_OUT_CHANNELS),
down_block_types=tuple(vae_config.get("down_block_types", ("DownEncoderBlock2D",))),
up_block_types=tuple(vae_config.get("up_block_types", ("UpDecoderBlock2D",))),
block_out_channels=tuple(vae_config.get("block_out_channels", (64,))),
layers_per_block=vae_config.get("layers_per_block", 1),
latent_channels=vae_config.get("latent_channels", DEFAULT_VAE_LATENT_CHANNELS),
norm_num_groups=vae_config.get("norm_num_groups", DEFAULT_VAE_NORM_NUM_GROUPS),
scaling_factor=vae_config.get("scaling_factor", DEFAULT_VAE_SCALING_FACTOR),
shift_factor=vae_config.get("shift_factor", None),
use_quant_conv=vae_config.get("use_quant_conv", True),
use_post_quant_conv=vae_config.get("use_post_quant_conv", True),
mid_block_add_attention=vae_config.get("mid_block_add_attention", True),
)
# VAE (fp32 for better precision)
vae_state_dict = load_sharded_safetensors(vae_dir, device="cpu")
vae.load_state_dict(vae_state_dict, strict=False)
del vae_state_dict
vae.to(device=device, dtype=torch.float32)
vae.eval()
torch.cuda.empty_cache()
# Text Encoder
if verbose:
logger.info("Loading Text Encoder...")
text_encoder_dir = model_dir / "text_encoder"
text_encoder = AutoModel.from_pretrained(
str(text_encoder_dir),
# torch_dtype=dtype, # some version use this
dtype=dtype,
trust_remote_code=True,
)
text_encoder.to(device)
text_encoder.eval()
# Tokenizer
os.environ["TOKENIZERS_PARALLELISM"] = "false"
if verbose:
logger.info("Loading Tokenizer...")
tokenizer_dir = model_dir / "tokenizer"
tokenizer = AutoTokenizer.from_pretrained(
str(tokenizer_dir) if tokenizer_dir.exists() else str(text_encoder_dir),
trust_remote_code=True,
)
# Scheduler
if verbose:
logger.info("Loading Scheduler...")
scheduler_dir = model_dir / "scheduler"
scheduler_config = load_config(str(scheduler_dir / "scheduler_config.json"))
scheduler = FlowMatchEulerDiscreteScheduler(
num_train_timesteps=scheduler_config.get("num_train_timesteps", DEFAULT_SCHEDULER_NUM_TRAIN_TIMESTEPS),
shift=scheduler_config.get("shift", DEFAULT_SCHEDULER_SHIFT),
use_dynamic_shifting=scheduler_config.get("use_dynamic_shifting", DEFAULT_SCHEDULER_USE_DYNAMIC_SHIFTING),
)
if compile:
if verbose:
logger.info("Compiling DiT and VAE...")
transformer = torch.compile(transformer)
vae = torch.compile(vae)
if verbose:
logger.success("All components loaded successfully")
return {
"transformer": transformer,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"scheduler": scheduler,
}
|