diffsynth/pipelines/stable_diffusion_xl.py

from ..models import ModelManager, SDXLTextEncoder, SDXLTextEncoder2, SDXLUNet, SDXLVAEDecoder, SDXLVAEEncoder
# TODO: SDXL ControlNet
from ..prompts import SDXLPrompter
from ..schedulers import EnhancedDDIMScheduler
import torch
from tqdm import tqdm
from PIL import Image
import numpy as np


class SDXLImagePipeline(torch.nn.Module):

    def __init__(self, device="cuda", torch_dtype=torch.float16):
        super().__init__()
        self.scheduler = EnhancedDDIMScheduler()
        self.prompter = SDXLPrompter()
        self.device = device
        self.torch_dtype = torch_dtype
        # models
        self.text_encoder: SDXLTextEncoder = None
        self.text_encoder_2: SDXLTextEncoder2 = None
        self.unet: SDXLUNet = None
        self.vae_decoder: SDXLVAEDecoder = None
        self.vae_encoder: SDXLVAEEncoder = None
        # TODO: SDXL ControlNet
    
    def fetch_main_models(self, model_manager: ModelManager):
        self.text_encoder = model_manager.text_encoder
        self.text_encoder_2 = model_manager.text_encoder_2
        self.unet = model_manager.unet
        self.vae_decoder = model_manager.vae_decoder
        self.vae_encoder = model_manager.vae_encoder
        # load textual inversion
        self.prompter.load_textual_inversion(model_manager.textual_inversion_dict)


    def fetch_controlnet_models(self, model_manager: ModelManager, **kwargs):
        # TODO: SDXL ControlNet
        pass


    def fetch_prompter(self, model_manager: ModelManager):
        self.prompter.load_from_model_manager(model_manager)


    @staticmethod
    def from_model_manager(model_manager: ModelManager, controlnet_config_units = [], **kwargs):
        pipe = SDXLImagePipeline(
            device=model_manager.device,
            torch_dtype=model_manager.torch_dtype,
        )
        pipe.fetch_main_models(model_manager)
        pipe.fetch_prompter(model_manager)
        pipe.fetch_controlnet_models(model_manager, controlnet_config_units=controlnet_config_units)
        return pipe
    

    def preprocess_image(self, image):
        image = torch.Tensor(np.array(image, dtype=np.float32) * (2 / 255) - 1).permute(2, 0, 1).unsqueeze(0)
        return image
    

    def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32):
        image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0]
        image = image.cpu().permute(1, 2, 0).numpy()
        image = Image.fromarray(((image / 2 + 0.5).clip(0, 1) * 255).astype("uint8"))
        return image
    

    @torch.no_grad()
    def __call__(
        self,
        prompt,
        negative_prompt="",
        cfg_scale=7.5,
        clip_skip=1,
        clip_skip_2=2,
        input_image=None,
        controlnet_image=None,
        denoising_strength=1.0,
        height=1024,
        width=1024,
        num_inference_steps=20,
        tiled=False,
        tile_size=64,
        tile_stride=32,
        progress_bar_cmd=tqdm,
        progress_bar_st=None,
    ):
        # Prepare scheduler
        self.scheduler.set_timesteps(num_inference_steps, denoising_strength)

        # Prepare latent tensors
        if input_image is not None:
            image = self.preprocess_image(input_image).to(device=self.device, dtype=self.torch_dtype)
            latents = self.vae_encoder(image.to(torch.float32), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(self.torch_dtype)
            noise = torch.randn((1, 4, height//8, width//8), device=self.device, dtype=self.torch_dtype)
            latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0])
        else:
            latents = torch.randn((1, 4, height//8, width//8), device=self.device, dtype=self.torch_dtype)

        # Encode prompts
        add_prompt_emb_posi, prompt_emb_posi = self.prompter.encode_prompt(
            self.text_encoder,
            self.text_encoder_2,
            prompt,
            clip_skip=clip_skip, clip_skip_2=clip_skip_2,
            device=self.device,
            positive=True,
        )
        if cfg_scale != 1.0:
            add_prompt_emb_nega, prompt_emb_nega = self.prompter.encode_prompt(
                self.text_encoder,
                self.text_encoder_2,
                negative_prompt,
                clip_skip=clip_skip, clip_skip_2=clip_skip_2,
                device=self.device,
                positive=False,
            )
        
        # Prepare scheduler
        self.scheduler.set_timesteps(num_inference_steps, denoising_strength)
        
        # Prepare positional id
        add_time_id = torch.tensor([height, width, 0, 0, height, width], device=self.device)
        
        # Denoise
        for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
            timestep = torch.IntTensor((timestep,))[0].to(self.device)

            # Classifier-free guidance
            if cfg_scale != 1.0:
                noise_pred_posi = self.unet(
                    latents, timestep, prompt_emb_posi,
                    add_time_id=add_time_id, add_text_embeds=add_prompt_emb_posi,
                    tiled=tiled, tile_size=tile_size, tile_stride=tile_stride
                )
                noise_pred_nega = self.unet(
                    latents, timestep, prompt_emb_nega,
                    add_time_id=add_time_id, add_text_embeds=add_prompt_emb_nega,
                    tiled=tiled, tile_size=tile_size, tile_stride=tile_stride
                )
                noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega)
            else:
                noise_pred = self.unet(
                    latents, timestep, prompt_emb_posi,
                    add_time_id=add_time_id, add_text_embeds=add_prompt_emb_posi,
                    tiled=tiled, tile_size=tile_size, tile_stride=tile_stride
                )

            latents = self.scheduler.step(noise_pred, timestep, latents)
            
            if progress_bar_st is not None:
                progress_bar_st.progress(progress_id / len(self.scheduler.timesteps))
        
        # Decode image
        image = self.decode_image(latents.to(torch.float32), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)

        return image