pipline_StableDiffusion_ConsistentID.py

from typing import Any, Callable, Dict, List, Optional, Union, Tuple
import cv2
import PIL
import numpy as np 
from PIL import Image
import torch
from torchvision import transforms
from insightface.app import FaceAnalysis 
### insight-face installation can be found at https://github.com/deepinsight/insightface
from safetensors import safe_open
from huggingface_hub.utils import validate_hf_hub_args
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion import StableDiffusionPipeline
from diffusers.utils import _get_model_file
from functions import process_text_with_markers, masks_for_unique_values, fetch_mask_raw_image, tokenize_and_mask_noun_phrases_ends, prepare_image_token_idx
from functions import ProjPlusModel, masks_for_unique_values
from attention import Consistent_IPAttProcessor, Consistent_AttProcessor, FacialEncoder
from huggingface_hub import hf_hub_download

PipelineImageInput = Union[
    PIL.Image.Image,
    torch.FloatTensor,
    List[PIL.Image.Image],
    List[torch.FloatTensor],
]

### Download the pretrained model from huggingface and put it locally, then place the model in a local directory and specify the directory location.
class ConsistentIDStableDiffusionPipeline(StableDiffusionPipeline):
    @validate_hf_hub_args
    def load_ConsistentID_model(
        self,
        pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
        bise_net,
        weight_name: str,
        subfolder: str = '',
        trigger_word_ID: str = '<|image|>',
        trigger_word_facial: str = '<|facial|>',
        image_encoder_path: str = 'laion/CLIP-ViT-H-14-laion2B-s32B-b79K',  
        torch_dtype = torch.float16,
        num_tokens = 4,
        lora_rank= 128,
        **kwargs,
    ):
        self.lora_rank = lora_rank 
        self.torch_dtype = torch_dtype
        self.num_tokens = num_tokens
        self.set_ip_adapter()
        self.image_encoder_path = image_encoder_path
        self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(self.image_encoder_path).to(
            self.device, dtype=self.torch_dtype
        )   
        self.clip_image_processor = CLIPImageProcessor()
        self.id_image_processor = CLIPImageProcessor()
        self.crop_size = 512

        # FaceID
        self.app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
        self.app.prepare(ctx_id=0, det_size=(640, 640))

        ### BiSeNet
        # self.bise_net = BiSeNet(n_classes = 19)
        # self.bise_net.cuda() # CUDA must not be initialized in the main process on Spaces with Stateless GPU environment
        # self.bise_net_cp=bise_net_cp_path
        # self.bise_net.load_state_dict(torch.load(self.bise_net_cp))
        self.bise_net = bise_net # load from outside
        self.bise_net.eval()
        # Colors for all 20 parts
        self.part_colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0],
                    [255, 0, 85], [255, 0, 170],
                    [0, 255, 0], [85, 255, 0], [170, 255, 0],
                    [0, 255, 85], [0, 255, 170],
                    [0, 0, 255], [85, 0, 255], [170, 0, 255],
                    [0, 85, 255], [0, 170, 255],
                    [255, 255, 0], [255, 255, 85], [255, 255, 170],
                    [255, 0, 255], [255, 85, 255], [255, 170, 255],
                    [0, 255, 255], [85, 255, 255], [170, 255, 255]]
        
        ### LLVA (Optional)
        self.llva_model_path = "liuhaotian/llava-v1.5-13b" # TODO 
        # IMPORTANT! Download the openai/clip-vit-large-patch14-336 model and specify the model path in config.json ("mm_vision_tower": "openai/clip-vit-large-patch14-336").
        self.llva_prompt = "Describe this person's facial features for me, including face, ears, eyes, nose, and mouth." 
        self.llva_tokenizer, self.llva_model, self.llva_image_processor, self.llva_context_len = None,None,None,None #load_pretrained_model(self.llva_model_path)

        self.image_proj_model = ProjPlusModel(
            cross_attention_dim=self.unet.config.cross_attention_dim, 
            id_embeddings_dim=512,
            clip_embeddings_dim=self.image_encoder.config.hidden_size, 
            num_tokens=self.num_tokens,  # 4 - inspirsed by IPAdapter and Midjourney
        ).to(self.device, dtype=self.torch_dtype)
        self.FacialEncoder = FacialEncoder(self.image_encoder).to(self.device, dtype=self.torch_dtype)

        # Load the main state dict first.
        cache_dir = kwargs.pop("cache_dir", None)
        force_download = kwargs.pop("force_download", False)
        resume_download = kwargs.pop("resume_download", False)
        proxies = kwargs.pop("proxies", None)
        local_files_only = kwargs.pop("local_files_only", None)
        token = kwargs.pop("token", None)
        revision = kwargs.pop("revision", None)

        user_agent = {
            "file_type": "attn_procs_weights",
            "framework": "pytorch",
        }

        if not isinstance(pretrained_model_name_or_path_or_dict, dict):
            model_file = _get_model_file(
                pretrained_model_name_or_path_or_dict,
                weights_name=weight_name,
                cache_dir=cache_dir,
                force_download=force_download,
                resume_download=resume_download,
                proxies=proxies,
                local_files_only=local_files_only,
                use_auth_token=token,
                revision=revision,
                subfolder=subfolder,
                user_agent=user_agent,
            )
            if weight_name.endswith(".safetensors"):
                state_dict = {"id_encoder": {}, "lora_weights": {}}
                with safe_open(model_file, framework="pt", device="cpu") as f:
                    ### TODO safetensors add
                    for key in f.keys():
                        if key.startswith("FacialEncoder."):
                            state_dict["FacialEncoder"][key.replace("FacialEncoder.", "")] = f.get_tensor(key)
                        elif key.startswith("image_proj."):
                            state_dict["image_proj"][key.replace("image_proj.", "")] = f.get_tensor(key)
            else:
                state_dict = torch.load(model_file, map_location="cpu")
        else:
            state_dict = pretrained_model_name_or_path_or_dict
    
        self.trigger_word_ID = trigger_word_ID
        self.trigger_word_facial = trigger_word_facial

        self.FacialEncoder.load_state_dict(state_dict["FacialEncoder"], strict=True)
        self.image_proj_model.load_state_dict(state_dict["image_proj"], strict=True)
        ip_layers = torch.nn.ModuleList(self.unet.attn_processors.values())
        ip_layers.load_state_dict(state_dict["adapter_modules"], strict=True)
        print(f"Successfully loaded weights from checkpoint")

        # Add trigger word token
        if self.tokenizer is not None: 
            self.tokenizer.add_tokens([self.trigger_word_ID], special_tokens=True)
            self.tokenizer.add_tokens([self.trigger_word_facial], special_tokens=True)

    def set_ip_adapter(self):
        unet = self.unet
        attn_procs = {}
        for name in unet.attn_processors.keys():
            cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
            if name.startswith("mid_block"):
                hidden_size = unet.config.block_out_channels[-1]
            elif name.startswith("up_blocks"):
                block_id = int(name[len("up_blocks.")])
                hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
            elif name.startswith("down_blocks"):
                block_id = int(name[len("down_blocks.")])
                hidden_size = unet.config.block_out_channels[block_id]
            if cross_attention_dim is None:
                attn_procs[name] = Consistent_AttProcessor(
                    hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=self.lora_rank,
                ).to(self.device, dtype=self.torch_dtype)
            else:
                attn_procs[name] = Consistent_IPAttProcessor(
                    hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, scale=1.0, rank=self.lora_rank, num_tokens=self.num_tokens,
                ).to(self.device, dtype=self.torch_dtype)
        
        unet.set_attn_processor(attn_procs)

    @torch.inference_mode()
    def get_facial_embeds(self, prompt_embeds, negative_prompt_embeds, facial_clip_images, facial_token_masks, valid_facial_token_idx_mask):
        
        hidden_states = []
        uncond_hidden_states = []
        for facial_clip_image in facial_clip_images:
            hidden_state = self.image_encoder(facial_clip_image.to(self.device, dtype=self.torch_dtype), output_hidden_states=True).hidden_states[-2]
            uncond_hidden_state = self.image_encoder(torch.zeros_like(facial_clip_image, dtype=self.torch_dtype).to(self.device), output_hidden_states=True).hidden_states[-2]
            hidden_states.append(hidden_state)
            uncond_hidden_states.append(uncond_hidden_state)
        multi_facial_embeds = torch.stack(hidden_states)       
        uncond_multi_facial_embeds = torch.stack(uncond_hidden_states)   

        # condition 
        facial_prompt_embeds = self.FacialEncoder(prompt_embeds, multi_facial_embeds, facial_token_masks, valid_facial_token_idx_mask)  

        # uncondition 
        uncond_facial_prompt_embeds = self.FacialEncoder(negative_prompt_embeds, uncond_multi_facial_embeds, facial_token_masks, valid_facial_token_idx_mask)  

        return facial_prompt_embeds, uncond_facial_prompt_embeds        

    @torch.inference_mode()   
    def get_image_embeds(self, faceid_embeds, face_image, s_scale, shortcut=False):

        clip_image = self.clip_image_processor(images=face_image, return_tensors="pt").pixel_values
        clip_image = clip_image.to(self.device, dtype=self.torch_dtype)
        clip_image_embeds = self.image_encoder(clip_image, output_hidden_states=True).hidden_states[-2]
        uncond_clip_image_embeds = self.image_encoder(torch.zeros_like(clip_image), output_hidden_states=True).hidden_states[-2]
        
        faceid_embeds = faceid_embeds.to(self.device, dtype=self.torch_dtype)
        image_prompt_tokens = self.image_proj_model(faceid_embeds, clip_image_embeds, shortcut=shortcut, scale=s_scale)
        uncond_image_prompt_embeds = self.image_proj_model(torch.zeros_like(faceid_embeds), uncond_clip_image_embeds, shortcut=shortcut, scale=s_scale)
        
        return image_prompt_tokens, uncond_image_prompt_embeds

    def set_scale(self, scale):
        for attn_processor in self.pipe.unet.attn_processors.values():
            if isinstance(attn_processor, Consistent_IPAttProcessor):
                attn_processor.scale = scale

    @torch.inference_mode()
    def get_prepare_faceid(self, face_image):
        faceid_image = np.array(face_image)
        faces = self.app.get(faceid_image)
        if faces==[]:
            faceid_embeds = torch.zeros_like(torch.empty((1, 512)))
        else:
            faceid_embeds = torch.from_numpy(faces[0].normed_embedding).unsqueeze(0)

        return faceid_embeds

    @torch.inference_mode()
    def parsing_face_mask(self, raw_image_refer):

        to_tensor = transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
        ])
        to_pil = transforms.ToPILImage()

        with torch.no_grad():
            image = raw_image_refer.resize((512, 512), Image.BILINEAR)
            image_resize_PIL = image
            img = to_tensor(image)
            img = torch.unsqueeze(img, 0)
            img = img.float().cuda()
            out = self.bise_net(img)[0]
            parsing_anno = out.squeeze(0).cpu().numpy().argmax(0)
        
        im = np.array(image_resize_PIL)
        vis_im = im.copy().astype(np.uint8)
        stride=1
        vis_parsing_anno = parsing_anno.copy().astype(np.uint8)
        vis_parsing_anno = cv2.resize(vis_parsing_anno, None, fx=stride, fy=stride, interpolation=cv2.INTER_NEAREST)
        vis_parsing_anno_color = np.zeros((vis_parsing_anno.shape[0], vis_parsing_anno.shape[1], 3)) + 255

        num_of_class = np.max(vis_parsing_anno)

        for pi in range(1, num_of_class + 1): # num_of_class=17 pi=1~16
            index = np.where(vis_parsing_anno == pi) 
            vis_parsing_anno_color[index[0], index[1], :] = self.part_colors[pi] 

        vis_parsing_anno_color = vis_parsing_anno_color.astype(np.uint8)
        vis_parsing_anno_color = cv2.addWeighted(cv2.cvtColor(vis_im, cv2.COLOR_RGB2BGR), 0.4, vis_parsing_anno_color, 0.6, 0)

        return vis_parsing_anno_color, vis_parsing_anno

    @torch.inference_mode()
    def get_prepare_llva_caption(self, input_image_file, model_path=None, prompt=None):
        
        ### Optional: Use the LLaVA
        # args = type('Args', (), {
        #     "model_path": self.llva_model_path,
        #     "model_base": None,
        #     "model_name": get_model_name_from_path(self.llva_model_path),
        #     "query": self.llva_prompt,
        #     "conv_mode": None,
        #     "image_file": input_image_file,
        #     "sep": ",",
        #     "temperature": 0,
        #     "top_p": None,
        #     "num_beams": 1,
        #     "max_new_tokens": 512
        # })() 
        # face_caption = eval_model(args, self.llva_tokenizer, self.llva_model, self.llva_image_processor)

        ### Use built-in template
        face_caption = "The person has one nose, two eyes, two ears, and a mouth."

        return face_caption

    @torch.inference_mode()
    def get_prepare_facemask(self, input_image_file):

        vis_parsing_anno_color, vis_parsing_anno = self.parsing_face_mask(input_image_file)
        parsing_mask_list = masks_for_unique_values(vis_parsing_anno) 

        key_parsing_mask_list = {}
        key_list = ["Face", "Left_Ear", "Right_Ear", "Left_Eye", "Right_Eye", "Nose", "Upper_Lip", "Lower_Lip"]
        processed_keys = set()
        for key, mask_image in parsing_mask_list.items():
            if key in key_list:
                if "_" in key:
                    prefix = key.split("_")[1]
                    if prefix in processed_keys:                   
                        continue
                    else:            
                        key_parsing_mask_list[key] = mask_image 
                        processed_keys.add(prefix)  
            
                key_parsing_mask_list[key] = mask_image            

        return key_parsing_mask_list, vis_parsing_anno_color

    def encode_prompt_with_trigger_word(
        self,
        prompt: str,
        face_caption: str,
        key_parsing_mask_list = None,
        image_token = "<|image|>", 
        facial_token = "<|facial|>",
        max_num_facials = 5,
        num_id_images: int = 1,
        device: Optional[torch.device] = None,
    ):
        device = device or self._execution_device

        face_caption_align, key_parsing_mask_list_align = process_text_with_markers(face_caption, key_parsing_mask_list) 
        
        prompt_face = prompt + "Detail:" + face_caption_align

        max_text_length=330      
        if len(self.tokenizer(prompt_face, max_length=self.tokenizer.model_max_length, padding="max_length",truncation=False,return_tensors="pt").input_ids[0])!=77:
            prompt_face = "Detail:" + face_caption_align + " Caption:" + prompt
        
        if len(face_caption)>max_text_length:
            prompt_face = prompt
            face_caption_align =  ""
  
        prompt_text_only = prompt_face.replace("<|facial|>", "").replace("<|image|>", "")
        tokenizer = self.tokenizer
        facial_token_id = tokenizer.convert_tokens_to_ids(facial_token)
        image_token_id = None

        clean_input_id, image_token_mask, facial_token_mask = tokenize_and_mask_noun_phrases_ends(
        prompt_face, image_token_id, facial_token_id, tokenizer) 

        image_token_idx, image_token_idx_mask, facial_token_idx, facial_token_idx_mask = prepare_image_token_idx(
            image_token_mask, facial_token_mask, num_id_images, max_num_facials )

        return prompt_text_only, clean_input_id, key_parsing_mask_list_align, facial_token_mask, facial_token_idx, facial_token_idx_mask

    @torch.inference_mode()
    def get_prepare_clip_image(self, input_image_file, key_parsing_mask_list, image_size=512, max_num_facials=5, change_facial=True):
        
        facial_mask = []
        facial_clip_image = []
        transform_mask = transforms.Compose([transforms.CenterCrop(size=image_size), transforms.ToTensor(),])
        clip_image_processor = CLIPImageProcessor()

        num_facial_part = len(key_parsing_mask_list)

        for key in key_parsing_mask_list:
            key_mask=key_parsing_mask_list[key]
            facial_mask.append(transform_mask(key_mask))
            key_mask_raw_image = fetch_mask_raw_image(input_image_file,key_mask)
            parsing_clip_image = clip_image_processor(images=key_mask_raw_image, return_tensors="pt").pixel_values
            facial_clip_image.append(parsing_clip_image)

        padding_ficial_clip_image = torch.zeros_like(torch.zeros([1, 3, 224, 224]))
        padding_ficial_mask = torch.zeros_like(torch.zeros([1, image_size, image_size]))

        if num_facial_part < max_num_facials:
            facial_clip_image += [torch.zeros_like(padding_ficial_clip_image) for _ in range(max_num_facials - num_facial_part) ]
            facial_mask += [ torch.zeros_like(padding_ficial_mask) for _ in range(max_num_facials - num_facial_part)]

        facial_clip_image = torch.stack(facial_clip_image, dim=1).squeeze(0)
        facial_mask = torch.stack(facial_mask, dim=0).squeeze(dim=1)

        return facial_clip_image, facial_mask

    @torch.no_grad()
    def __call__(
        self,
        prompt: Union[str, List[str]] = None,
        height: Optional[int] = None,
        width: Optional[int] = None,
        num_inference_steps: int = 50,
        guidance_scale: float = 5.0,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        eta: float = 0.0,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        latents: Optional[torch.FloatTensor] = None,
        prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        original_size: Optional[Tuple[int, int]] = None,
        target_size: Optional[Tuple[int, int]] = None,
        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
        callback_steps: int = 1,
        input_id_images: PipelineImageInput = None,
        start_merge_step: int = 0,
        class_tokens_mask: Optional[torch.LongTensor] = None,
        prompt_embeds_text_only: Optional[torch.FloatTensor] = None,
    ):
        # 0. Default height and width to unet
        height = height or self.unet.config.sample_size * self.vae_scale_factor
        width = width or self.unet.config.sample_size * self.vae_scale_factor

        original_size = original_size or (height, width)
        target_size = target_size or (height, width)

        # 1. Check inputs. Raise error if not correct
        self.check_inputs(
            prompt,
            height,
            width,
            callback_steps,
            negative_prompt,
            prompt_embeds,
            negative_prompt_embeds,
        )
        if not isinstance(input_id_images, list):
            input_id_images = [input_id_images]

        # 2. Define call parameters
        if prompt is not None and isinstance(prompt, str):
            batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        device = self._execution_device
        do_classifier_free_guidance = guidance_scale >= 1.0
        input_image_file = input_id_images[0]

        faceid_embeds = self.get_prepare_faceid(face_image=input_image_file)
        face_caption = self.get_prepare_llva_caption(input_image_file)
        key_parsing_mask_list, vis_parsing_anno_color = self.get_prepare_facemask(input_image_file)

        assert do_classifier_free_guidance

        # 3. Encode input prompt
        num_id_images = len(input_id_images)

        (
            prompt_text_only,
            clean_input_id,
            key_parsing_mask_list_align,
            facial_token_mask,
            facial_token_idx,
            facial_token_idx_mask,
        ) = self.encode_prompt_with_trigger_word(
            prompt = prompt,
            face_caption = face_caption,
            # prompt_2=None,  
            key_parsing_mask_list=key_parsing_mask_list,
            device=device,
            max_num_facials = 5,
            num_id_images= num_id_images,
            # prompt_embeds= None,
            # pooled_prompt_embeds= None,
            # class_tokens_mask= None,
        )

        # 4. Encode input prompt without the trigger word for delayed conditioning
        encoder_hidden_states = self.text_encoder(clean_input_id.to(device))[0] 

        prompt_embeds = self._encode_prompt(
            prompt_text_only,
            device=device,
            num_images_per_prompt=num_images_per_prompt,
            do_classifier_free_guidance=True,
            negative_prompt=negative_prompt,
        )
        negative_encoder_hidden_states_text_only = prompt_embeds[0:num_images_per_prompt]
        encoder_hidden_states_text_only = prompt_embeds[num_images_per_prompt:]

        # 5. Prepare the input ID images
        prompt_tokens_faceid, uncond_prompt_tokens_faceid = self.get_image_embeds(faceid_embeds, face_image=input_image_file, s_scale=1.0, shortcut=False)

        facial_clip_image, facial_mask = self.get_prepare_clip_image(input_image_file, key_parsing_mask_list_align, image_size=512, max_num_facials=5)
        facial_clip_images = facial_clip_image.unsqueeze(0).to(device, dtype=self.torch_dtype)
        facial_token_mask = facial_token_mask.to(device)
        facial_token_idx_mask = facial_token_idx_mask.to(device)
        negative_encoder_hidden_states = negative_encoder_hidden_states_text_only

        cross_attention_kwargs = {}

        # 6. Get the update text embedding
        prompt_embeds_facial, uncond_prompt_embeds_facial = self.get_facial_embeds(encoder_hidden_states, negative_encoder_hidden_states, \
                                                            facial_clip_images, facial_token_mask, facial_token_idx_mask)

        prompt_embeds = torch.cat([prompt_embeds_facial, prompt_tokens_faceid], dim=1)
        negative_prompt_embeds = torch.cat([uncond_prompt_embeds_facial, uncond_prompt_tokens_faceid], dim=1)

        prompt_embeds = self._encode_prompt(
            prompt,
            device,
            num_images_per_prompt,
            do_classifier_free_guidance,
            negative_prompt,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
        )        
        prompt_embeds_text_only = torch.cat([encoder_hidden_states_text_only, prompt_tokens_faceid], dim=1)
        prompt_embeds = torch.cat([prompt_embeds, prompt_embeds_text_only], dim=0)

        # 7. Prepare timesteps
        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps = self.scheduler.timesteps

        # 8. Prepare latent variables
        num_channels_latents = self.unet.in_channels
        latents = self.prepare_latents(
            batch_size * num_images_per_prompt,
            num_channels_latents,
            height,
            width,
            prompt_embeds.dtype,
            device,
            generator,
            latents,
        )

        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
        (
            null_prompt_embeds,
            augmented_prompt_embeds,
            text_prompt_embeds,
        ) = prompt_embeds.chunk(3)

        # 9. Denoising loop
        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                latent_model_input = (
                    torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                )
                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
                
                if i <= start_merge_step:
                    current_prompt_embeds = torch.cat(
                        [null_prompt_embeds, text_prompt_embeds], dim=0
                    )
                else:
                    current_prompt_embeds = torch.cat(
                        [null_prompt_embeds, augmented_prompt_embeds], dim=0
                    )

                # predict the noise residual
                noise_pred = self.unet(
                    latent_model_input,
                    t,
                    encoder_hidden_states=current_prompt_embeds,
                    cross_attention_kwargs=cross_attention_kwargs,
                ).sample

                # perform guidance
                if do_classifier_free_guidance:
                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                    noise_pred = noise_pred_uncond + guidance_scale * (
                        noise_pred_text - noise_pred_uncond
                    )
                else:
                    assert 0, "Not Implemented"

                # compute the previous noisy sample x_t -> x_t-1
                latents = self.scheduler.step(
                    noise_pred, t, latents, **extra_step_kwargs
                ).prev_sample

                # call the callback, if provided
                if i == len(timesteps) - 1 or (
                    (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
                ):
                    progress_bar.update()
                    if callback is not None and i % callback_steps == 0:
                        callback(i, t, latents)

        if output_type == "latent":
            image = latents
            has_nsfw_concept = None
        elif output_type == "pil":
            # 9.1 Post-processing
            image = self.decode_latents(latents)

            # 9.2 Run safety checker
            image, has_nsfw_concept = self.run_safety_checker(
                image, device, prompt_embeds.dtype
            )

            # 9.3 Convert to PIL
            image = self.numpy_to_pil(image)
        else:
            # 9.1 Post-processing
            image = self.decode_latents(latents)

            # 9.2 Run safety checker
            image, has_nsfw_concept = self.run_safety_checker(
                image, device, prompt_embeds.dtype
            )

        # Offload last model to CPU
        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
            self.final_offload_hook.offload()

        if not return_dict:
            return (image, has_nsfw_concept)

        return StableDiffusionPipelineOutput(
            images=image, nsfw_content_detected=has_nsfw_concept
        )