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