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1ed770c 8d69668 1ed770c 8d69668 1ed770c | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 | """IRDiffAE: standalone HuggingFace-compatible iRDiffAE model."""
from __future__ import annotations
from pathlib import Path
import torch
from torch import Tensor, nn
from .config import IRDiffAEConfig, IRDiffAEInferenceConfig
from .decoder import Decoder
from .encoder import Encoder
from .samplers import run_ddim, run_dpmpp_2m
from .vp_diffusion import get_schedule, make_initial_state, sample_noise
def _resolve_model_dir(
path_or_repo_id: str | Path,
*,
revision: str | None,
cache_dir: str | Path | None,
) -> Path:
"""Resolve a local path or HuggingFace Hub repo ID to a local directory."""
local = Path(path_or_repo_id)
if local.is_dir():
return local
# Not a local directory — try HuggingFace Hub
repo_id = str(path_or_repo_id)
try:
from huggingface_hub import snapshot_download
except ImportError:
raise ImportError(
f"'{repo_id}' is not an existing local directory. "
"To download from HuggingFace Hub, install huggingface_hub: "
"pip install huggingface_hub"
)
cache_dir_str = str(cache_dir) if cache_dir is not None else None
local_dir = snapshot_download(
repo_id,
revision=revision,
cache_dir=cache_dir_str,
)
return Path(local_dir)
class IRDiffAE(nn.Module):
"""Standalone iRDiffAE model for HuggingFace distribution.
A diffusion autoencoder that encodes images to compact latents and
decodes them back via iterative VP diffusion.
Usage::
model = IRDiffAE.from_pretrained("path/to/weights")
model = model.to("cuda", dtype=torch.bfloat16)
# Encode
latents = model.encode(images) # images: [B,3,H,W] in [-1,1]
# Decode (1 step by default — PSNR-optimal)
recon = model.decode(latents, height=H, width=W)
# Reconstruct (encode + 1-step decode)
recon = model.reconstruct(images)
"""
def __init__(self, config: IRDiffAEConfig) -> None:
super().__init__()
self.config = config
self.encoder = Encoder(
in_channels=config.in_channels,
patch_size=config.patch_size,
model_dim=config.model_dim,
depth=config.encoder_depth,
bottleneck_dim=config.bottleneck_dim,
mlp_ratio=config.mlp_ratio,
depthwise_kernel_size=config.depthwise_kernel_size,
)
self.decoder = Decoder(
in_channels=config.in_channels,
patch_size=config.patch_size,
model_dim=config.model_dim,
depth=config.decoder_depth,
bottleneck_dim=config.bottleneck_dim,
mlp_ratio=config.mlp_ratio,
depthwise_kernel_size=config.depthwise_kernel_size,
adaln_low_rank_rank=config.adaln_low_rank_rank,
)
@classmethod
def from_pretrained(
cls,
path_or_repo_id: str | Path,
*,
dtype: torch.dtype = torch.bfloat16,
device: str | torch.device = "cpu",
revision: str | None = None,
cache_dir: str | Path | None = None,
) -> IRDiffAE:
"""Load a pretrained model from a local directory or HuggingFace Hub.
The directory (or repo) should contain:
- config.json: Model architecture config.
- model.safetensors (preferred) or model.pt: Model weights.
Args:
path_or_repo_id: Local directory path or HuggingFace Hub repo ID
(e.g. ``"data-archetype/irdiffae-v1"``).
dtype: Load weights in this dtype (float32 or bfloat16).
device: Target device.
revision: Git revision (branch, tag, or commit) for Hub downloads.
cache_dir: Where to cache Hub downloads. Uses HF default if None.
Returns:
Loaded model in eval mode.
"""
model_dir = _resolve_model_dir(
path_or_repo_id, revision=revision, cache_dir=cache_dir
)
config = IRDiffAEConfig.load(model_dir / "config.json")
model = cls(config)
# Try safetensors first, fall back to .pt
safetensors_path = model_dir / "model.safetensors"
pt_path = model_dir / "model.pt"
if safetensors_path.exists():
try:
from safetensors.torch import load_file
state_dict = load_file(str(safetensors_path), device=str(device))
except ImportError:
raise ImportError(
"safetensors package required to load .safetensors files. "
"Install with: pip install safetensors"
)
elif pt_path.exists():
state_dict = torch.load(
str(pt_path), map_location=device, weights_only=True
)
else:
raise FileNotFoundError(
f"No model weights found in {model_dir}. "
"Expected model.safetensors or model.pt."
)
model.load_state_dict(state_dict)
model = model.to(dtype=dtype, device=torch.device(device))
model.eval()
return model
def encode(self, images: Tensor) -> Tensor:
"""Encode images to latents.
Args:
images: [B, 3, H, W] in [-1, 1], H and W must be divisible by patch_size.
Returns:
Latents [B, bottleneck_dim, H/patch, W/patch].
"""
try:
model_dtype = next(self.parameters()).dtype
except StopIteration:
model_dtype = torch.float32
return self.encoder(images.to(dtype=model_dtype))
@torch.no_grad()
def decode(
self,
latents: Tensor,
height: int,
width: int,
*,
inference_config: IRDiffAEInferenceConfig | None = None,
) -> Tensor:
"""Decode latents to images via VP diffusion.
Args:
latents: [B, bottleneck_dim, h, w] encoder latents.
height: Output image height (must be divisible by patch_size).
width: Output image width (must be divisible by patch_size).
inference_config: Optional inference parameters. Uses defaults if None.
Returns:
Reconstructed images [B, 3, H, W] in float32.
"""
cfg = inference_config or IRDiffAEInferenceConfig()
config = self.config
batch = int(latents.shape[0])
device = latents.device
# Determine model dtype from parameters
try:
model_dtype = next(self.parameters()).dtype
except StopIteration:
model_dtype = torch.float32
# Validate dimensions
if height % config.patch_size != 0 or width % config.patch_size != 0:
raise ValueError(
f"height={height} and width={width} must be divisible by patch_size={config.patch_size}"
)
# Generate initial noise
shape = (batch, config.in_channels, height, width)
noise = sample_noise(
shape,
noise_std=config.pixel_noise_std,
seed=cfg.seed,
device=torch.device("cpu"),
dtype=torch.float32,
)
# Build schedule
schedule = get_schedule(cfg.schedule, cfg.num_steps).to(device=device)
# Construct initial state: sigma_start * noise
initial_state = make_initial_state(
noise=noise.to(device=device),
t_start=schedule[0:1],
logsnr_min=config.logsnr_min,
logsnr_max=config.logsnr_max,
)
# Disable autocast for numerical precision
device_type = "cuda" if device.type == "cuda" else "cpu"
with torch.autocast(device_type=device_type, enabled=False):
latents_in = latents.to(device=device)
def _forward_fn(
x_t: Tensor,
t: Tensor,
latents: Tensor,
*,
drop_middle_blocks: bool = False,
) -> Tensor:
return self.decoder(
x_t.to(dtype=model_dtype),
t,
latents.to(dtype=model_dtype),
drop_middle_blocks=drop_middle_blocks,
)
# Select sampler
if cfg.sampler == "ddim":
sampler_fn = run_ddim
elif cfg.sampler == "dpmpp_2m":
sampler_fn = run_dpmpp_2m
else:
raise ValueError(
f"Unsupported sampler: {cfg.sampler!r}. Use 'ddim' or 'dpmpp_2m'."
)
result = sampler_fn(
forward_fn=_forward_fn,
initial_state=initial_state,
schedule=schedule,
latents=latents_in,
logsnr_min=config.logsnr_min,
logsnr_max=config.logsnr_max,
pdg_enabled=cfg.pdg_enabled,
pdg_strength=cfg.pdg_strength,
device=device,
)
return result
@torch.no_grad()
def reconstruct(
self,
images: Tensor,
*,
inference_config: IRDiffAEInferenceConfig | None = None,
) -> Tensor:
"""Encode then decode. Convenience wrapper.
Args:
images: [B, 3, H, W] in [-1, 1].
inference_config: Optional inference parameters.
Returns:
Reconstructed images [B, 3, H, W] in float32.
"""
latents = self.encode(images)
_, _, h, w = images.shape
return self.decode(
latents, height=h, width=w, inference_config=inference_config
)
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