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 PIL import Image from polygraphy import cuda from streamdiffusion import StreamDiffusion from streamdiffusion.image_utils import postprocess_image torch.set_grad_enabled(False) torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True class StreamDiffusionWrapper: def __init__( self, model_id_or_path: str, t_index_list: List[int], lora_dict: Optional[Dict[str, float]] = None, mode: Literal["img2img", "txt2img"] = "img2img", 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"] = "tensorrt", 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", seed: int = 2, use_safety_checker: bool = False, ): """ Initializes the StreamDiffusionWrapper. 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 ,...} mode : Literal["img2img", "txt2img"], optional txt2img or img2img, by default "img2img". output_type : Literal["pil", "pt", "np", "latent"], optional The output type of image, by default "pil". lcm_lora_id : Optional[str], optional The lcm_lora_id to load, by default None. If None, the default LCM-LoRA ("latent-consistency/lcm-lora-sdv1-5") will be used. vae_id : Optional[str], optional The vae_id to load, by default None. If None, the default TinyVAE ("madebyollin/taesd") will be used. device : Literal["cpu", "cuda"], optional The device to use for inference, by default "cuda". dtype : torch.dtype, optional The dtype for inference, by default torch.float16. frame_buffer_size : int, optional The frame buffer size 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 "tensorrt". do_add_noise : bool, optional Whether to add noise for following denoising steps or not, by default True. device_ids : Optional[List[int]], optional The device ids to use for DataParallel, by default None. 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. enable_similar_image_filter : bool, optional Whether to enable similar image filter or not, by default False. similar_image_filter_threshold : float, optional The threshold for similar image filter, by default 0.98. similar_image_filter_max_skip_frame : int, optional The max skip frame for similar image filter, by default 10. use_denoising_batch : bool, optional Whether to use denoising batch or not, by default True. cfg_type : Literal["none", "full", "self", "initialize"], optional The cfg_type for img2img mode, by default "self". You cannot use anything other than "none" for txt2img mode. seed : int, optional The seed, by default 2. use_safety_checker : bool, optional Whether to use safety checker or not, by default False. """ self.sd_turbo = "turbo" in model_id_or_path if mode == "txt2img": if cfg_type != "none": raise ValueError( f"txt2img mode accepts only cfg_type = 'none', but got {cfg_type}" ) if use_denoising_batch and frame_buffer_size > 1: if not self.sd_turbo: raise ValueError( "txt2img mode cannot use denoising batch with frame_buffer_size > 1." ) if mode == "img2img": if not use_denoising_batch: raise NotImplementedError( "img2img mode must use denoising batch for now." ) self.device = device self.dtype = dtype self.width = width self.height = height self.mode = mode 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_safety_checker = use_safety_checker self.stream: StreamDiffusion = 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, ) 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: Optional[Union[str, Image.Image, torch.Tensor]] = None, prompt: Optional[str] = None, ) -> Union[Image.Image, List[Image.Image]]: """ Performs img2img or txt2img based on the mode. 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. """ if self.mode == "img2img": return self.img2img(image) else: return self.txt2img(prompt) def txt2img( self, prompt: Optional[str] = None ) -> Union[Image.Image, List[Image.Image], torch.Tensor, np.ndarray]: """ Performs txt2img. Parameters ---------- prompt : Optional[str] The prompt to generate images from. Returns ------- Union[Image.Image, List[Image.Image]] The generated image. """ if prompt is not None: self.stream.update_prompt(prompt) if self.sd_turbo: image_tensor = self.stream.txt2img_sd_turbo(self.batch_size) else: image_tensor = self.stream.txt2img(self.frame_buffer_size) 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 img2img( self, image: Union[str, Image.Image, torch.Tensor] ) -> 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 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", "sfast", "tensorrt"] = "tensorrt", 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, ) -> StreamDiffusion: """ 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 "self". You cannot use anything other than "none" for txt2img mode. seed : int, optional The seed, by default 2. Returns ------- StreamDiffusion 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 = StreamDiffusion( 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 ) else: stream.load_lcm_lora() stream.fuse_lora() if lora_dict is not None: for lora_name, lora_scale in lora_dict.items(): stream.load_lora(lora_name) stream.fuse_lora(lora_scale=lora_scale) print(f"Use LoRA: {lora_name} in weights {lora_scale}") 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 acceleration == "tensorrt": from streamdiffusion.acceleration.tensorrt import ( TorchVAEEncoder, compile_unet, compile_vae_decoder, compile_vae_encoder, ) from streamdiffusion.acceleration.tensorrt.engine import ( AutoencoderKLEngine, UNet2DConditionModelEngine, ) from streamdiffusion.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}--mode-{self.mode}" 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}--mode-{self.mode}" engine_dir = os.path.join("engines") 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=self.batch_size if self.mode == "txt2img" else stream.frame_bff_size, min_batch_size=self.batch_size if self.mode == "txt2img" else 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=self.batch_size if self.mode == "txt2img" else stream.frame_bff_size, min_batch_size=self.batch_size if self.mode == "txt2img" else 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=self.batch_size if self.mode == "txt2img" else stream.frame_bff_size, min_batch_size=self.batch_size if self.mode == "txt2img" else 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=self.batch_size if self.mode == "txt2img" else 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=self.batch_size if self.mode == "txt2img" else stream.frame_bff_size, min_batch_size=self.batch_size if self.mode == "txt2img" else 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=self.batch_size if self.mode == "txt2img" else 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": from streamdiffusion.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.") 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