File size: 20,186 Bytes
55d907c b1bdeca 55d907c |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 |
# -*- coding: utf-8 -*-
"""Diffusion pipeline configuration module."""
import gc
import typing as tp
from dataclasses import dataclass, field
import torch
from diffusers.pipelines import (
AutoPipelineForText2Image,
DiffusionPipeline,
FluxKontextPipeline,
FluxControlPipeline,
FluxFillPipeline,
SanaPipeline,
)
from omniconfig import configclass
from torch import nn
from transformers import PreTrainedModel, PreTrainedTokenizer, T5EncoderModel
from deepcompressor.data.utils.dtype import eval_dtype
from deepcompressor.quantizer.processor import Quantizer
from deepcompressor.utils import tools
from deepcompressor.utils.hooks import AccumBranchHook, ProcessHook
from ....nn.patch.linear import ConcatLinear, ShiftedLinear
from ....nn.patch.lowrank import LowRankBranch
from ..nn.patch import (
replace_fused_linear_with_concat_linear,
replace_up_block_conv_with_concat_conv,
shift_input_activations,
)
__all__ = ["DiffusionPipelineConfig"]
@configclass
@dataclass
class LoRAConfig:
"""LoRA configuration.
Args:
path (`str`):
The path of the LoRA branch.
weight_name (`str`):
The weight name of the LoRA branch.
alpha (`float`):
The alpha value of the LoRA branch.
"""
path: str
weight_name: str
alpha: float = 1.0
@configclass
@dataclass
class DiffusionPipelineConfig:
"""Diffusion pipeline configuration.
Args:
name (`str`):
The name of the pipeline.
dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):
The data type of the pipeline.
device (`str`, *optional*, defaults to `"cuda"`):
The device of the pipeline.
shift_activations (`bool`, *optional*, defaults to `False`):
Whether to shift activations.
"""
_pipeline_factories: tp.ClassVar[
dict[str, tp.Callable[[str, str, torch.dtype, torch.device, bool], DiffusionPipeline]]
] = {}
_text_extractors: tp.ClassVar[
dict[
str,
tp.Callable[
[DiffusionPipeline, tuple[type[PreTrainedModel], ...]],
list[tuple[str, PreTrainedModel, PreTrainedTokenizer]],
],
]
] = {}
name: str
path: str = ""
dtype: torch.dtype = field(
default_factory=lambda s=torch.float32: eval_dtype(s, with_quant_dtype=False, with_none=False)
)
device: str = "cuda"
shift_activations: bool = False
lora: LoRAConfig | None = None
family: str = field(init=False)
task: str = "text-to-image"
def __post_init__(self):
self.family = self.name.split("-")[0]
if self.name == "flux.1-canny-dev":
self.task = "canny-to-image"
elif self.name == "flux.1-depth-dev":
self.task = "depth-to-image"
elif self.name == "flux.1-fill-dev":
self.task = "inpainting"
def build(
self, *, dtype: str | torch.dtype | None = None, device: str | torch.device | None = None
) -> DiffusionPipeline:
"""Build the diffusion pipeline.
Args:
dtype (`str` or `torch.dtype`, *optional*):
The data type of the pipeline.
device (`str` or `torch.device`, *optional*):
The device of the pipeline.
Returns:
`DiffusionPipeline`:
The diffusion pipeline.
"""
if dtype is None:
dtype = self.dtype
if device is None:
device = self.device
_factory = self._pipeline_factories.get(self.name, self._default_build)
return _factory(
name=self.name, path=self.path, dtype=dtype, device=device, shift_activations=self.shift_activations
)
def extract_text_encoders(
self, pipeline: DiffusionPipeline, supported: tuple[type[PreTrainedModel], ...] = (T5EncoderModel,)
) -> list[tuple[str, PreTrainedModel, PreTrainedTokenizer]]:
"""Extract the text encoders and tokenizers from the pipeline.
Args:
pipeline (`DiffusionPipeline`):
The diffusion pipeline.
supported (`tuple[type[PreTrainedModel], ...]`, *optional*, defaults to `(T5EncoderModel,)`):
The supported text encoder types. If not specified, all text encoders will be extracted.
Returns:
`list[tuple[str, PreTrainedModel, PreTrainedTokenizer]]`:
The list of text encoder name, model, and tokenizer.
"""
_extractor = self._text_extractors.get(self.name, self._default_extract_text_encoders)
return _extractor(pipeline, supported)
@classmethod
def register_pipeline_factory(
cls,
names: str | tuple[str, ...],
/,
factory: tp.Callable[[str, str, torch.dtype, torch.device, bool], DiffusionPipeline],
*,
overwrite: bool = False,
) -> None:
"""Register a pipeline factory.
Args:
names (`str` or `tuple[str, ...]`):
The name of the pipeline.
factory (`Callable[[str, str,torch.dtype, torch.device, bool], DiffusionPipeline]`):
The pipeline factory function.
overwrite (`bool`, *optional*, defaults to `False`):
Whether to overwrite the existing factory for the pipeline.
"""
if isinstance(names, str):
names = [names]
for name in names:
if name in cls._pipeline_factories and not overwrite:
raise ValueError(f"Pipeline factory {name} already exists.")
cls._pipeline_factories[name] = factory
@classmethod
def register_text_extractor(
cls,
names: str | tuple[str, ...],
/,
extractor: tp.Callable[
[DiffusionPipeline, tuple[type[PreTrainedModel], ...]],
list[tuple[str, PreTrainedModel, PreTrainedTokenizer]],
],
*,
overwrite: bool = False,
) -> None:
"""Register a text extractor.
Args:
names (`str` or `tuple[str, ...]`):
The name of the pipeline.
extractor (`Callable[[DiffusionPipeline], list[tuple[str, PreTrainedModel, PreTrainedTokenizer]]`):
The text extractor function.
overwrite (`bool`, *optional*, defaults to `False`):
Whether to overwrite the existing extractor for the pipeline.
"""
if isinstance(names, str):
names = [names]
for name in names:
if name in cls._text_extractors and not overwrite:
raise ValueError(f"Text extractor {name} already exists.")
cls._text_extractors[name] = extractor
def load_lora( # noqa: C901
self, pipeline: DiffusionPipeline, smooth_cache: dict[str, torch.Tensor] | None = None
) -> DiffusionPipeline:
smooth_cache = smooth_cache or {}
model = pipeline.unet if hasattr(pipeline, "unet") else pipeline.transformer
assert isinstance(model, nn.Module)
if self.lora is not None:
logger = tools.logging.getLogger(__name__)
logger.info(f"Load LoRA branches from {self.lora.path}")
lora_state_dict, alphas = pipeline.lora_state_dict(
self.lora.path, return_alphas=True, weight_name=self.lora.weight_name
)
tools.logging.Formatter.indent_inc()
for name, module in model.named_modules():
if isinstance(module, (nn.Linear, ConcatLinear, ShiftedLinear)):
lora_a_key, lora_b_key = f"transformer.{name}.lora_A.weight", f"transformer.{name}.lora_B.weight"
if lora_a_key in lora_state_dict:
assert lora_b_key in lora_state_dict
logger.info(f"+ Load LoRA branch for {name}")
tools.logging.Formatter.indent_inc()
a = lora_state_dict.pop(lora_a_key)
b = lora_state_dict.pop(lora_b_key)
assert isinstance(a, torch.Tensor)
assert isinstance(b, torch.Tensor)
assert a.shape[1] == module.in_features
assert b.shape[0] == module.out_features
if isinstance(module, ConcatLinear):
logger.debug(
f"- split LoRA branch into {len(module.linears)} parts ({module.in_features_list})"
)
m_splits = module.linears
a_splits = a.split(module.in_features_list, dim=1)
b_splits = [b] * len(a_splits)
else:
m_splits, a_splits, b_splits = [module], [a], [b]
for m, a, b in zip(m_splits, a_splits, b_splits, strict=True):
assert a.shape[0] == b.shape[1]
if isinstance(m, ShiftedLinear):
s, m = m.shift, m.linear
logger.debug(f"- shift LoRA input by {s.item() if s.numel() == 1 else s}")
else:
s = None
assert isinstance(m, nn.Linear)
device, dtype = m.weight.device, m.weight.dtype
a, b = a.to(device=device, dtype=torch.float64), b.to(device=device, dtype=torch.float64)
if s is not None:
if s.numel() == 1:
s = torch.matmul(b, a.sum(dim=1).mul_(s.double())).mul_(self.lora.alpha)
else:
s = torch.matmul(b, torch.matmul(a, s.view(1, -1).double())).mul_(self.lora.alpha)
if hasattr(m, "in_smooth_cache_key"):
logger.debug(f"- smooth LoRA input using {m.in_smooth_cache_key} smooth scale")
ss = smooth_cache[m.in_smooth_cache_key].to(device=device, dtype=torch.float64)
a = a.mul_(ss.view(1, -1))
del ss
if hasattr(m, "out_smooth_cache_key"):
logger.debug(f"- smooth LoRA output using {m.out_smooth_cache_key} smooth scale")
ss = smooth_cache[m.out_smooth_cache_key].to(device=device, dtype=torch.float64)
b = b.div_(ss.view(-1, 1))
if s is not None:
s = s.div_(ss.view(-1))
del ss
branch_hook, quant_hook = None, None
for hook in m._forward_pre_hooks.values():
if isinstance(hook, AccumBranchHook) and isinstance(hook.branch, LowRankBranch):
branch_hook = hook
if isinstance(hook, ProcessHook) and isinstance(hook.processor, Quantizer):
quant_hook = hook
if branch_hook is not None:
logger.debug("- fuse with existing LoRA branch")
assert isinstance(branch_hook.branch, LowRankBranch)
_a = branch_hook.branch.a.weight.data
_b = branch_hook.branch.b.weight.data
if branch_hook.branch.alpha != self.lora.alpha:
a, b = a.to(dtype=dtype), b.mul_(self.lora.alpha).to(dtype=dtype)
_b = _b.to(dtype=torch.float64).mul_(branch_hook.branch.alpha).to(dtype=dtype)
alpha = 1
else:
a, b = a.to(dtype=dtype), b.to(dtype=dtype)
alpha = self.lora.alpha
branch_hook.branch = LowRankBranch(
m.in_features,
m.out_features,
rank=a.shape[0] + branch_hook.branch.rank,
alpha=alpha,
).to(device=device, dtype=dtype)
branch_hook.branch.a.weight.data[: a.shape[0], :] = a
branch_hook.branch.b.weight.data[:, : b.shape[1]] = b
branch_hook.branch.a.weight.data[a.shape[0] :, :] = _a
branch_hook.branch.b.weight.data[:, b.shape[1] :] = _b
else:
logger.debug("- create a new LoRA branch")
branch = LowRankBranch(
m.in_features, m.out_features, rank=a.shape[0], alpha=self.lora.alpha
)
branch = branch.to(device=device, dtype=dtype)
branch.a.weight.data.copy_(a.to(dtype=dtype))
branch.b.weight.data.copy_(b.to(dtype=dtype))
# low rank branch hook should be registered before the quantization hook
if quant_hook is not None:
logger.debug(f"- remove quantization hook from {name}")
quant_hook.remove(m)
logger.debug(f"- register LoRA branch to {name}")
branch.as_hook().register(m)
if quant_hook is not None:
logger.debug(f"- re-register quantization hook to {name}")
quant_hook.register(m)
if s is not None:
assert m.bias is not None
m.bias.data.copy_((m.bias.double().sub_(s)).to(dtype))
del m_splits, a_splits, b_splits, a, b, s
gc.collect()
torch.cuda.empty_cache()
tools.logging.Formatter.indent_dec()
tools.logging.Formatter.indent_dec()
if len(lora_state_dict) > 0:
logger.warning(f"Unused LoRA weights: {lora_state_dict.keys()}")
branches = nn.ModuleList()
for _, module in model.named_modules():
for hook in module._forward_hooks.values():
if isinstance(hook, AccumBranchHook) and isinstance(hook.branch, LowRankBranch):
branches.append(hook.branch)
model.register_module("_low_rank_branches", branches)
@staticmethod
def _default_build(
name: str, path: str, dtype: str | torch.dtype, device: str | torch.device, shift_activations: bool
) -> DiffusionPipeline:
if not path:
if name == "sdxl":
path = "stabilityai/stable-diffusion-xl-base-1.0"
elif name == "sdxl-turbo":
path = "stabilityai/sdxl-turbo"
elif name == "pixart-sigma":
path = "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS"
elif name == "flux.1-kontext-dev":
path = "black-forest-labs/FLUX.1-Kontext-dev"
elif name == "flux.1-dev":
path = "black-forest-labs/FLUX.1-dev"
elif name == "flux.1-canny-dev":
path = "black-forest-labs/FLUX.1-Canny-dev"
elif name == "flux.1-depth-dev":
path = "black-forest-labs/FLUX.1-Depth-dev"
elif name == "flux.1-fill-dev":
path = "black-forest-labs/FLUX.1-Fill-dev"
elif name == "flux.1-schnell":
path = "black-forest-labs/FLUX.1-schnell"
else:
raise ValueError(f"Path for {name} is not specified.")
if name in ["flux.1-kontext-dev"]:
pipeline = FluxKontextPipeline.from_pretrained(path, torch_dtype=dtype)
elif name in ["flux.1-canny-dev", "flux.1-depth-dev"]:
pipeline = FluxControlPipeline.from_pretrained(path, torch_dtype=dtype)
elif name == "flux.1-fill-dev":
pipeline = FluxFillPipeline.from_pretrained(path, torch_dtype=dtype)
elif name.startswith("sana-"):
if dtype == torch.bfloat16:
pipeline = SanaPipeline.from_pretrained(path, variant="bf16", torch_dtype=dtype, use_safetensors=True)
pipeline.vae.to(dtype)
pipeline.text_encoder.to(dtype)
else:
pipeline = SanaPipeline.from_pretrained(path, torch_dtype=dtype)
else:
pipeline = AutoPipelineForText2Image.from_pretrained(path, torch_dtype=dtype)
# Debug output
print(">>> DEVICE:", device)
print(">>> PIPELINE TYPE:", type(pipeline))
# Try to move each component using .to_empty()
for name in ["unet", "transformer", "vae", "text_encoder"]:
module = getattr(pipeline, name, None)
if isinstance(module, torch.nn.Module):
try:
print(f">>> Moving {name} to {device} using to_empty()")
module.to_empty(device=device) # Use keyword argument
except Exception as e:
print(f">>> WARNING: {name}.to_empty({device}) failed: {e}")
try:
print(f">>> Falling back to {name}.to({device})")
module.to(device)
except Exception as ee:
print(f">>> ERROR: {name}.to({device}) also failed: {ee}")
# Identify main model (for patching)
model = getattr(pipeline, "unet", None) or getattr(pipeline, "transformer", None)
if model is not None:
replace_fused_linear_with_concat_linear(model)
replace_up_block_conv_with_concat_conv(model)
if shift_activations:
shift_input_activations(model)
else:
print(">>> WARNING: No model (unet/transformer) found for patching")
return pipeline
@staticmethod
def _default_extract_text_encoders(
pipeline: DiffusionPipeline, supported: tuple[type[PreTrainedModel], ...]
) -> list[tuple[str, PreTrainedModel, PreTrainedTokenizer]]:
"""Extract the text encoders and tokenizers from the pipeline.
Args:
pipeline (`DiffusionPipeline`):
The diffusion pipeline.
supported (`tuple[type[PreTrainedModel], ...]`, *optional*, defaults to `(T5EncoderModel,)`):
The supported text encoder types. If not specified, all text encoders will be extracted.
Returns:
`list[tuple[str, PreTrainedModel, PreTrainedTokenizer]]`:
The list of text encoder name, model, and tokenizer.
"""
results: list[tuple[str, PreTrainedModel, PreTrainedTokenizer]] = []
for key in vars.__dict__.keys():
if key.startswith("text_encoder"):
suffix = key[len("text_encoder") :]
encoder, tokenizer = getattr(pipeline, f"text_encoder{suffix}"), getattr(pipeline, f"tokenizer{suffix}")
if not supported or isinstance(encoder, supported):
results.append((key, encoder, tokenizer))
return results
|