server/wrapper.py

import gc
import os
import traceback
from typing import List, Literal, Optional, Union

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: str,
        t_index_list: List[int],
        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", "sfast", "tensorrt"] = "xformers",
        is_drawing: 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,
        use_denoising_batch: bool = True,
        cfg_type: Literal["none", "full", "self", "initialize"] = "none",
        use_safety_checker: bool = False,
    ):
        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:
                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.sd_turbo = "turbo" in model_id
        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 = self._load_model(
            model_id=model_id,
            lcm_lora_id=lcm_lora_id,
            vae_id=vae_id,
            t_index_list=t_index_list,
            acceleration=acceleration,
            warmup=warmup,
            is_drawing=is_drawing,
            use_lcm_lora=use_lcm_lora,
            use_tiny_vae=use_tiny_vae,
            cfg_type=cfg_type,
        )

        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)

    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.
        """
        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: str
    ) -> Union[Image.Image, List[Image.Image], torch.Tensor, np.ndarray]:
        """
        Performs txt2img.

        Parameters
        ----------
        prompt : str
            The prompt to generate images from.

        Returns
        -------
        Union[Image.Image, List[Image.Image]]
            The generated image.
        """
        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)
        return self.postprocess_image(image_tensor, output_type=self.output_type)

    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: str,
        t_index_list: List[int],
        lcm_lora_id: Optional[str] = None,
        vae_id: Optional[str] = None,
        acceleration: Literal["none", "sfast", "tensorrt"] = "tensorrt",
        is_drawing: bool = True,
        warmup: int = 10,
        use_lcm_lora: bool = True,
        use_tiny_vae: bool = True,
        cfg_type: Literal["none", "full", "self", "initialize"] = "self",
    ):
        """
        Loads the model.

        This method does the following:

        1. Loads the model from the model_id.
        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. Warms up the model.

        Parameters
        ----------
        model_id : str
            The model id to load.
        t_index_list : List[int]
            The t_index_list to use for inference.
        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 to use, by default "tensorrt".
        warmup : int, optional
            The number of warmup steps to perform, by default 10.
        is_drawing : bool, optional
            Whether to draw the image 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 to use, by default "self".
        """

        try:  # Load from local directory
            pipe: StableDiffusionPipeline = StableDiffusionPipeline.from_pretrained(
                model_id,
            ).to(device=self.device, dtype=self.dtype)

        except ValueError:  # Load from huggingface
            pipe: StableDiffusionPipeline = StableDiffusionPipeline.from_single_file(
                model_id
            ).to(device=self.device, dtype=self.dtype)
        except Exception:  # No model found
            traceback.print_exc()
            print("Model load has failed. Doesn't exist.")
            exit()

        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))

        stream = StreamDiffusion(
            pipe=pipe,
            t_index_list=t_index_list,
            torch_dtype=self.dtype,
            width=self.width,
            height=self.height,
            is_drawing=is_drawing,
            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 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(
                    max_batch_size: int,
                    min_batch_size: int,
                ):
                    return f"{model_id}--lcm_lora-{use_tiny_vae}--tiny_vae-{use_lcm_lora}--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(
                        stream.trt_unet_batch_size, stream.trt_unet_batch_size
                    ),
                    "unet.engine",
                )
                vae_encoder_path = os.path.join(
                    engine_dir,
                    create_prefix(
                        self.batch_size
                        if self.mode == "txt2img"
                        else stream.frame_bff_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(
                        self.batch_size
                        if self.mode == "txt2img"
                        else stream.frame_bff_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(2),
        )

        return stream