Spaces:
Runtime error
Runtime error
File size: 28,370 Bytes
e08f02e |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 |
import gc
import os
from pathlib import Path
import traceback
from typing import List, Literal, Optional, Union, Dict
import numpy as np
import torch
from diffusers import AutoencoderTiny, StableDiffusionPipeline
from diffusers.models.attention_processor import XFormersAttnProcessor, AttnProcessor2_0
from PIL import Image
from streamv2v import StreamV2V
from streamv2v.image_utils import postprocess_image
from streamv2v.models.attention_processor import CachedSTXFormersAttnProcessor, CachedSTAttnProcessor2_0
torch.set_grad_enabled(False)
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
class StreamV2VWrapper:
def __init__(
self,
model_id_or_path: str,
t_index_list: List[int],
lora_dict: Optional[Dict[str, float]] = None,
output_type: Literal["pil", "pt", "np", "latent"] = "pil",
lcm_lora_id: Optional[str] = None,
vae_id: Optional[str] = None,
device: Literal["cpu", "cuda"] = "cuda",
dtype: torch.dtype = torch.float16,
frame_buffer_size: int = 1,
width: int = 512,
height: int = 512,
warmup: int = 10,
acceleration: Literal["none", "xformers", "tensorrt"] = "xformers",
do_add_noise: bool = True,
device_ids: Optional[List[int]] = None,
use_lcm_lora: bool = True,
use_tiny_vae: bool = True,
enable_similar_image_filter: bool = False,
similar_image_filter_threshold: float = 0.98,
similar_image_filter_max_skip_frame: int = 10,
use_denoising_batch: bool = True,
cfg_type: Literal["none", "full", "self", "initialize"] = "self",
use_cached_attn: bool = True,
use_feature_injection: bool = True,
feature_injection_strength: float = 0.8,
feature_similarity_threshold: float = 0.98,
cache_interval: int = 4,
cache_maxframes: int = 1,
use_tome_cache: bool = True,
tome_metric: str = "keys",
tome_ratio: float = 0.5,
use_grid: bool = False,
seed: int = 2,
use_safety_checker: bool = False,
engine_dir: Optional[Union[str, Path]] = "engines",
):
"""
Initializes the StreamV2VWrapper.
Parameters
----------
model_id_or_path : str
The model identifier or path to load.
t_index_list : List[int]
The list of indices to use for inference.
lora_dict : Optional[Dict[str, float]], optional
Dictionary of LoRA names and their corresponding scales,
by default None. Example: {'LoRA_1': 0.5, 'LoRA_2': 0.7, ...}
output_type : Literal["pil", "pt", "np", "latent"], optional
The type of output image, by default "pil".
lcm_lora_id : Optional[str], optional
The identifier for the LCM-LoRA to load, by default None.
If None, the default LCM-LoRA ("latent-consistency/lcm-lora-sdv1-5") is used.
vae_id : Optional[str], optional
The identifier for the VAE to load, by default None.
If None, the default TinyVAE ("madebyollin/taesd") is used.
device : Literal["cpu", "cuda"], optional
The device to use for inference, by default "cuda".
dtype : torch.dtype, optional
The data type for inference, by default torch.float16.
frame_buffer_size : int, optional
The size of the frame buffer for denoising batch, by default 1.
width : int, optional
The width of the image, by default 512.
height : int, optional
The height of the image, by default 512.
warmup : int, optional
The number of warmup steps to perform, by default 10.
acceleration : Literal["none", "xformers", "tensorrt"], optional
The acceleration method, by default "xformers".
do_add_noise : bool, optional
Whether to add noise during denoising steps, by default True.
device_ids : Optional[List[int]], optional
List of device IDs to use for DataParallel, by default None.
use_lcm_lora : bool, optional
Whether to use LCM-LoRA, by default True.
use_tiny_vae : bool, optional
Whether to use TinyVAE, by default True.
enable_similar_image_filter : bool, optional
Whether to enable similar image filtering, by default False.
similar_image_filter_threshold : float, optional
The threshold for the similar image filter, by default 0.98.
similar_image_filter_max_skip_frame : int, optional
The maximum number of frames to skip for similar image filter, by default 10.
use_denoising_batch : bool, optional
Whether to use denoising batch, by default True.
cfg_type : Literal["none", "full", "self", "initialize"], optional
The CFG type for img2img mode, by default "self".
use_cached_attn : bool, optional
Whether to cache self-attention maps from previous frames to improve temporal consistency, by default True.
use_feature_injection : bool, optional
Whether to use feature maps from previous frames to improve temporal consistency, by default True.
feature_injection_strength : float, optional
The strength of feature injection, by default 0.8.
feature_similarity_threshold : float, optional
The similarity threshold for feature injection, by default 0.98.
cache_interval : int, optional
The interval at which to cache attention maps, by default 4.
cache_maxframes : int, optional
The maximum number of frames to cache attention maps, by default 1.
use_tome_cache : bool, optional
Whether to use Tome caching, by default True.
tome_metric : str, optional
The metric to use for Tome, by default "keys".
tome_ratio : float, optional
The ratio for Tome, by default 0.5.
use_grid : bool, optional
Whether to use grid, by default False.
seed : int, optional
The seed for random number generation, by default 2.
use_safety_checker : bool, optional
Whether to use a safety checker, by default False.
engine_dir : Optional[Union[str, Path]], optional
The directory for the engine, by default "engines".
"""
# TODO: Test SD turbo
self.sd_turbo = "turbo" in model_id_or_path
assert use_denoising_batch, "vid2vid mode must use denoising batch for now."
self.device = device
self.dtype = dtype
self.width = width
self.height = height
self.output_type = output_type
self.frame_buffer_size = frame_buffer_size
self.batch_size = (
len(t_index_list) * frame_buffer_size
if use_denoising_batch
else frame_buffer_size
)
self.use_denoising_batch = use_denoising_batch
self.use_cached_attn = use_cached_attn
self.use_feature_injection = use_feature_injection
self.feature_injection_strength = feature_injection_strength
self.feature_similarity_threshold = feature_similarity_threshold
self.cache_interval = cache_interval
self.cache_maxframes = cache_maxframes
self.use_tome_cache = use_tome_cache
self.tome_metric = tome_metric
self.tome_ratio = tome_ratio
self.use_grid = use_grid
self.use_safety_checker = use_safety_checker
self.stream: StreamV2V = self._load_model(
model_id_or_path=model_id_or_path,
lora_dict=lora_dict,
lcm_lora_id=lcm_lora_id,
vae_id=vae_id,
t_index_list=t_index_list,
acceleration=acceleration,
warmup=warmup,
do_add_noise=do_add_noise,
use_lcm_lora=use_lcm_lora,
use_tiny_vae=use_tiny_vae,
cfg_type=cfg_type,
seed=seed,
engine_dir=engine_dir,
)
if device_ids is not None:
self.stream.unet = torch.nn.DataParallel(
self.stream.unet, device_ids=device_ids
)
if enable_similar_image_filter:
self.stream.enable_similar_image_filter(similar_image_filter_threshold, similar_image_filter_max_skip_frame)
def prepare(
self,
prompt: str,
negative_prompt: str = "",
num_inference_steps: int = 50,
guidance_scale: float = 1.2,
delta: float = 1.0,
) -> None:
"""
Prepares the model for inference.
Parameters
----------
prompt : str
The prompt to generate images from.
num_inference_steps : int, optional
The number of inference steps to perform, by default 50.
guidance_scale : float, optional
The guidance scale to use, by default 1.2.
delta : float, optional
The delta multiplier of virtual residual noise,
by default 1.0.
"""
self.stream.prepare(
prompt,
negative_prompt,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
delta=delta,
)
def __call__(
self,
image: Union[str, Image.Image, torch.Tensor],
prompt: Optional[str] = None,
) -> Union[Image.Image, List[Image.Image]]:
"""
Performs img2img
Parameters
----------
image : Optional[Union[str, Image.Image, torch.Tensor]]
The image to generate from.
prompt : Optional[str]
The prompt to generate images from.
Returns
-------
Union[Image.Image, List[Image.Image]]
The generated image.
"""
return self.img2img(image, prompt)
def img2img(
self, image: Union[str, Image.Image, torch.Tensor], prompt: Optional[str] = None
) -> Union[Image.Image, List[Image.Image], torch.Tensor, np.ndarray]:
"""
Performs img2img.
Parameters
----------
image : Union[str, Image.Image, torch.Tensor]
The image to generate from.
Returns
-------
Image.Image
The generated image.
"""
if prompt is not None:
self.stream.update_prompt(prompt)
if isinstance(image, str) or isinstance(image, Image.Image):
image = self.preprocess_image(image)
image_tensor = self.stream(image)
image = self.postprocess_image(image_tensor, output_type=self.output_type)
if self.use_safety_checker:
safety_checker_input = self.feature_extractor(
image, return_tensors="pt"
).to(self.device)
_, has_nsfw_concept = self.safety_checker(
images=image_tensor.to(self.dtype),
clip_input=safety_checker_input.pixel_values.to(self.dtype),
)
image = self.nsfw_fallback_img if has_nsfw_concept[0] else image
return image
def preprocess_image(self, image: Union[str, Image.Image]) -> torch.Tensor:
"""
Preprocesses the image.
Parameters
----------
image : Union[str, Image.Image, torch.Tensor]
The image to preprocess.
Returns
-------
torch.Tensor
The preprocessed image.
"""
if isinstance(image, str):
image = Image.open(image).convert("RGB").resize((self.width, self.height))
if isinstance(image, Image.Image):
image = image.convert("RGB").resize((self.width, self.height))
return self.stream.image_processor.preprocess(
image, self.height, self.width
).to(device=self.device, dtype=self.dtype)
def postprocess_image(
self, image_tensor: torch.Tensor, output_type: str = "pil"
) -> Union[Image.Image, List[Image.Image], torch.Tensor, np.ndarray]:
"""
Postprocesses the image.
Parameters
----------
image_tensor : torch.Tensor
The image tensor to postprocess.
Returns
-------
Union[Image.Image, List[Image.Image]]
The postprocessed image.
"""
if self.frame_buffer_size > 1:
return postprocess_image(image_tensor.cpu(), output_type=output_type)
else:
return postprocess_image(image_tensor.cpu(), output_type=output_type)[0]
def _load_model(
self,
model_id_or_path: str,
t_index_list: List[int],
lora_dict: Optional[Dict[str, float]] = None,
lcm_lora_id: Optional[str] = None,
vae_id: Optional[str] = None,
acceleration: Literal["none", "xformers", "tensorrt"] = "xformers",
warmup: int = 10,
do_add_noise: bool = True,
use_lcm_lora: bool = True,
use_tiny_vae: bool = True,
cfg_type: Literal["none", "full", "self", "initialize"] = "self",
seed: int = 2,
engine_dir: Optional[Union[str, Path]] = "engines",
) -> StreamV2V:
"""
Loads the model.
This method does the following:
1. Loads the model from the model_id_or_path.
2. Loads and fuses the LCM-LoRA model from the lcm_lora_id if needed.
3. Loads the VAE model from the vae_id if needed.
4. Enables acceleration if needed.
5. Prepares the model for inference.
6. Load the safety checker if needed.
Parameters
----------
model_id_or_path : str
The model id or path to load.
t_index_list : List[int]
The t_index_list to use for inference.
lora_dict : Optional[Dict[str, float]], optional
The lora_dict to load, by default None.
Keys are the LoRA names and values are the LoRA scales.
Example: {'LoRA_1' : 0.5 , 'LoRA_2' : 0.7 ,...}
lcm_lora_id : Optional[str], optional
The lcm_lora_id to load, by default None.
vae_id : Optional[str], optional
The vae_id to load, by default None.
acceleration : Literal["none", "xfomers", "sfast", "tensorrt"], optional
The acceleration method, by default "tensorrt".
warmup : int, optional
The number of warmup steps to perform, by default 10.
do_add_noise : bool, optional
Whether to add noise for following denoising steps or not,
by default True.
use_lcm_lora : bool, optional
Whether to use LCM-LoRA or not, by default True.
use_tiny_vae : bool, optional
Whether to use TinyVAE or not, by default True.
cfg_type : Literal["none", "full", "self", "initialize"],
optional
The cfg_type for img2img mode, by default " seed : int, optional
".
seed : int, optional
The seed, by default 2.
Returns
-------
StreamV2V
The loaded model.
"""
try: # Load from local directory
pipe: StableDiffusionPipeline = StableDiffusionPipeline.from_pretrained(
model_id_or_path,
).to(device=self.device, dtype=self.dtype)
except ValueError: # Load from huggingface
pipe: StableDiffusionPipeline = StableDiffusionPipeline.from_single_file(
model_id_or_path,
).to(device=self.device, dtype=self.dtype)
except Exception: # No model found
traceback.print_exc()
print("Model load has failed. Doesn't exist.")
exit()
stream = StreamV2V(
pipe=pipe,
t_index_list=t_index_list,
torch_dtype=self.dtype,
width=self.width,
height=self.height,
do_add_noise=do_add_noise,
frame_buffer_size=self.frame_buffer_size,
use_denoising_batch=self.use_denoising_batch,
cfg_type=cfg_type,
)
if not self.sd_turbo:
if use_lcm_lora:
if lcm_lora_id is not None:
stream.load_lcm_lora(
pretrained_model_name_or_path_or_dict=lcm_lora_id,
adapter_name="lcm")
else:
stream.load_lcm_lora(
pretrained_model_name_or_path_or_dict="latent-consistency/lcm-lora-sdv1-5",
adapter_name="lcm"
)
if lora_dict is not None:
for lora_name, lora_scale in lora_dict.items():
stream.load_lora(lora_name)
if use_tiny_vae:
if vae_id is not None:
stream.vae = AutoencoderTiny.from_pretrained(vae_id).to(
device=pipe.device, dtype=pipe.dtype
)
else:
stream.vae = AutoencoderTiny.from_pretrained("madebyollin/taesd").to(
device=pipe.device, dtype=pipe.dtype
)
try:
if acceleration == "xformers":
stream.pipe.enable_xformers_memory_efficient_attention()
if self.use_cached_attn:
attn_processors = stream.pipe.unet.attn_processors
new_attn_processors = {}
for key, attn_processor in attn_processors.items():
assert isinstance(attn_processor, XFormersAttnProcessor), \
"We only replace 'XFormersAttnProcessor' to 'CachedSTXFormersAttnProcessor'"
new_attn_processors[key] = CachedSTXFormersAttnProcessor(name=key,
use_feature_injection=self.use_feature_injection,
feature_injection_strength=self.feature_injection_strength,
feature_similarity_threshold=self.feature_similarity_threshold,
interval=self.cache_interval,
max_frames=self.cache_maxframes,
use_tome_cache=self.use_tome_cache,
tome_metric=self.tome_metric,
tome_ratio=self.tome_ratio,
use_grid=self.use_grid)
stream.pipe.unet.set_attn_processor(new_attn_processors)
if acceleration == "tensorrt":
if self.use_cached_attn:
raise NotImplementedError("TensorRT seems not support the costom attention_processor")
else:
stream.pipe.enable_xformers_memory_efficient_attention()
if self.use_cached_attn:
attn_processors = stream.pipe.unet.attn_processors
new_attn_processors = {}
for key, attn_processor in attn_processors.items():
assert isinstance(attn_processor, XFormersAttnProcessor), \
"We only replace 'XFormersAttnProcessor' to 'CachedSTXFormersAttnProcessor'"
new_attn_processors[key] = CachedSTXFormersAttnProcessor(name=key,
use_feature_injection=self.use_feature_injection,
feature_injection_strength=self.feature_injection_strength,
feature_similarity_threshold=self.feature_similarity_threshold,
interval=self.cache_interval,
max_frames=self.cache_maxframes,
use_tome_cache=self.use_tome_cache,
tome_metric=self.tome_metric,
tome_ratio=self.tome_ratio,
use_grid=self.use_grid)
stream.pipe.unet.set_attn_processor(new_attn_processors)
from polygraphy import cuda
from streamv2v.acceleration.tensorrt import (
TorchVAEEncoder,
compile_unet,
compile_vae_decoder,
compile_vae_encoder,
)
from streamv2v.acceleration.tensorrt.engine import (
AutoencoderKLEngine,
UNet2DConditionModelEngine,
)
from streamv2v.acceleration.tensorrt.models import (
VAE,
UNet,
VAEEncoder,
)
def create_prefix(
model_id_or_path: str,
max_batch_size: int,
min_batch_size: int,
):
maybe_path = Path(model_id_or_path)
if maybe_path.exists():
return f"{maybe_path.stem}--lcm_lora-{use_lcm_lora}--tiny_vae-{use_tiny_vae}--max_batch-{max_batch_size}--min_batch-{min_batch_size}--cache--{self.use_cached_attn}"
else:
return f"{model_id_or_path}--lcm_lora-{use_lcm_lora}--tiny_vae-{use_tiny_vae}--max_batch-{max_batch_size}--min_batch-{min_batch_size}--cache--{self.use_cached_attn}"
engine_dir = Path(engine_dir)
unet_path = os.path.join(
engine_dir,
create_prefix(
model_id_or_path=model_id_or_path,
max_batch_size=stream.trt_unet_batch_size,
min_batch_size=stream.trt_unet_batch_size,
),
"unet.engine",
)
vae_encoder_path = os.path.join(
engine_dir,
create_prefix(
model_id_or_path=model_id_or_path,
max_batch_size=stream.frame_bff_size,
min_batch_size=stream.frame_bff_size,
),
"vae_encoder.engine",
)
vae_decoder_path = os.path.join(
engine_dir,
create_prefix(
model_id_or_path=model_id_or_path,
max_batch_size=stream.frame_bff_size,
min_batch_size=stream.frame_bff_size,
),
"vae_decoder.engine",
)
if not os.path.exists(unet_path):
os.makedirs(os.path.dirname(unet_path), exist_ok=True)
unet_model = UNet(
fp16=True,
device=stream.device,
max_batch_size=stream.trt_unet_batch_size,
min_batch_size=stream.trt_unet_batch_size,
embedding_dim=stream.text_encoder.config.hidden_size,
unet_dim=stream.unet.config.in_channels,
)
compile_unet(
stream.unet,
unet_model,
unet_path + ".onnx",
unet_path + ".opt.onnx",
unet_path,
opt_batch_size=stream.trt_unet_batch_size,
)
if not os.path.exists(vae_decoder_path):
os.makedirs(os.path.dirname(vae_decoder_path), exist_ok=True)
stream.vae.forward = stream.vae.decode
vae_decoder_model = VAE(
device=stream.device,
max_batch_size=stream.frame_bff_size,
min_batch_size=stream.frame_bff_size,
)
compile_vae_decoder(
stream.vae,
vae_decoder_model,
vae_decoder_path + ".onnx",
vae_decoder_path + ".opt.onnx",
vae_decoder_path,
opt_batch_size=stream.frame_bff_size,
)
delattr(stream.vae, "forward")
if not os.path.exists(vae_encoder_path):
os.makedirs(os.path.dirname(vae_encoder_path), exist_ok=True)
vae_encoder = TorchVAEEncoder(stream.vae).to(torch.device("cuda"))
vae_encoder_model = VAEEncoder(
device=stream.device,
max_batch_size=stream.frame_bff_size,
min_batch_size=stream.frame_bff_size,
)
compile_vae_encoder(
vae_encoder,
vae_encoder_model,
vae_encoder_path + ".onnx",
vae_encoder_path + ".opt.onnx",
vae_encoder_path,
opt_batch_size=stream.frame_bff_size,
)
cuda_steram = cuda.Stream()
vae_config = stream.vae.config
vae_dtype = stream.vae.dtype
stream.unet = UNet2DConditionModelEngine(
unet_path, cuda_steram, use_cuda_graph=False
)
stream.vae = AutoencoderKLEngine(
vae_encoder_path,
vae_decoder_path,
cuda_steram,
stream.pipe.vae_scale_factor,
use_cuda_graph=False,
)
setattr(stream.vae, "config", vae_config)
setattr(stream.vae, "dtype", vae_dtype)
gc.collect()
torch.cuda.empty_cache()
print("TensorRT acceleration enabled.")
if acceleration == "sfast":
if self.use_cached_attn:
raise NotImplementedError
from streamv2v.acceleration.sfast import (
accelerate_with_stable_fast,
)
stream = accelerate_with_stable_fast(stream)
print("StableFast acceleration enabled.")
except Exception:
traceback.print_exc()
print("Acceleration has failed. Falling back to normal mode.")
if seed < 0: # Random seed
seed = np.random.randint(0, 1000000)
stream.prepare(
"",
"",
num_inference_steps=50,
guidance_scale=1.1
if stream.cfg_type in ["full", "self", "initialize"]
else 1.0,
generator=torch.manual_seed(seed),
seed=seed,
)
if self.use_safety_checker:
from transformers import CLIPFeatureExtractor
from diffusers.pipelines.stable_diffusion.safety_checker import (
StableDiffusionSafetyChecker,
)
self.safety_checker = StableDiffusionSafetyChecker.from_pretrained(
"CompVis/stable-diffusion-safety-checker"
).to(pipe.device)
self.feature_extractor = CLIPFeatureExtractor.from_pretrained(
"openai/clip-vit-base-patch32"
)
self.nsfw_fallback_img = Image.new("RGB", (512, 512), (0, 0, 0))
return stream
|