Update app.py

app.py

@@ -1001,7 +1001,579 @@ use_safetensors= False
 # # pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
 # pipe1.scheduler.set_timesteps(50)
 ### 
-pipe2 = StableDiffusionXLPipeline.from_pretrained(
+
+from typing import Any, Callable, Dict, List, Optional, Union, Tuple
+from collections import OrderedDict
+import os
+import PIL
+import numpy as np
+
+import torch
+from torchvision import transforms as T
+
+from safetensors import safe_open
+from huggingface_hub.utils import validate_hf_hub_args
+from transformers import CLIPImageProcessor, CLIPTokenizer
+from diffusers import StableDiffusionXLPipeline
+from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput
+from diffusers.utils import (
+    _get_model_file,
+    is_transformers_available,
+    logging,
+)
+
+from . import PhotoMakerIDEncoder
+
+PipelineImageInput = Union[
+    PIL.Image.Image,
+    torch.FloatTensor,
+    List[PIL.Image.Image],
+    List[torch.FloatTensor],
+]
+
+
+class PhotoMakerStableDiffusionXLPipeline(StableDiffusionXLPipeline):
+    @validate_hf_hub_args
+    def load_photomaker_adapter(
+        self,
+        pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
+        weight_name: str,
+        subfolder: str = '',
+        trigger_word: str = 'img',
+        **kwargs,
+    ):
+        """
+        Parameters:
+            pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
+                Can be either:
+                    - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on
+                      the Hub.
+                    - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved
+                      with [`ModelMixin.save_pretrained`].
+                    - A [torch state
+                      dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).
+            weight_name (`str`):
+                The weight name NOT the path to the weight.
+            subfolder (`str`, defaults to `""`):
+                The subfolder location of a model file within a larger model repository on the Hub or locally.
+            trigger_word (`str`, *optional*, defaults to `"img"`):
+                The trigger word is used to identify the position of class word in the text prompt,
+                and it is recommended not to set it as a common word.
+                This trigger word must be placed after the class word when used, otherwise, it will affect the performance of the personalized generation.
+        """
+
+        # 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,
+                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:
+                    for key in f.keys():
+                        if key.startswith("id_encoder."):
+                            state_dict["id_encoder"][key.replace("id_encoder.", "")] = f.get_tensor(key)
+                        elif key.startswith("lora_weights."):
+                            state_dict["lora_weights"][key.replace("lora_weights.", "")] = f.get_tensor(key)
+            else:
+                state_dict = torch.load(model_file, map_location="cpu")
+        else:
+            state_dict = pretrained_model_name_or_path_or_dict
+
+        keys = list(state_dict.keys())
+        if keys != ["id_encoder", "lora_weights"]:
+            raise ValueError("Required keys are (`id_encoder` and `lora_weights`) missing from the state dict.")
+
+        self.trigger_word = trigger_word
+        # load finetuned CLIP image encoder and fuse module here if it has not been registered to the pipeline yet
+        print(f"Loading PhotoMaker components [1] id_encoder from [{pretrained_model_name_or_path_or_dict}]...")
+        id_encoder = PhotoMakerIDEncoder()
+        id_encoder.load_state_dict(state_dict["id_encoder"], strict=True)
+        id_encoder = id_encoder.to(self.device, dtype=self.unet.dtype)
+        self.id_encoder = id_encoder
+        self.id_image_processor = CLIPImageProcessor()
+
+        # load lora into models
+        print(f"Loading PhotoMaker components [2] lora_weights from [{pretrained_model_name_or_path_or_dict}]")
+        self.load_lora_weights(state_dict["lora_weights"], adapter_name="photomaker")
+
+        # Add trigger word token
+        if self.tokenizer is not None:
+            self.tokenizer.add_tokens([self.trigger_word], special_tokens=True)
+
+        self.tokenizer_2.add_tokens([self.trigger_word], special_tokens=True)
+
+
+    def encode_prompt_with_trigger_word(
+        self,
+        prompt: str,
+        prompt_2: Optional[str] = None,
+        num_id_images: int = 1,
+        device: Optional[torch.device] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        class_tokens_mask: Optional[torch.LongTensor] = None,
+    ):
+        device = device or self._execution_device
+
+        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]
+
+        # Find the token id of the trigger word
+        image_token_id = self.tokenizer_2.convert_tokens_to_ids(self.trigger_word)
+
+        # Define tokenizers and text encoders
+        tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
+        text_encoders = (
+            [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
+        )
+
+        if prompt_embeds is None:
+            prompt_2 = prompt_2 or prompt
+            prompt_embeds_list = []
+            prompts = [prompt, prompt_2]
+            for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
+                input_ids = tokenizer.encode(prompt) # TODO: batch encode
+                clean_index = 0
+                clean_input_ids = []
+                class_token_index = []
+                # Find out the corresponding class word token based on the newly added trigger word token
+                for i, token_id in enumerate(input_ids):
+                    if token_id == image_token_id:
+                        class_token_index.append(clean_index - 1)
+                    else:
+                        clean_input_ids.append(token_id)
+                        clean_index += 1
+
+                if len(class_token_index) != 1:
+                    raise ValueError(
+                        f"PhotoMaker currently does not support multiple trigger words in a single prompt.\
+                            Trigger word: {self.trigger_word}, Prompt: {prompt}."
+                    )
+                class_token_index = class_token_index[0]
+
+                # Expand the class word token and corresponding mask
+                class_token = clean_input_ids[class_token_index]
+                clean_input_ids = clean_input_ids[:class_token_index] + [class_token] * num_id_images + \
+                    clean_input_ids[class_token_index+1:]
+
+                # Truncation or padding
+                max_len = tokenizer.model_max_length
+                if len(clean_input_ids) > max_len:
+                    clean_input_ids = clean_input_ids[:max_len]
+                else:
+                    clean_input_ids = clean_input_ids + [tokenizer.pad_token_id] * (
+                        max_len - len(clean_input_ids)
+                    )
+
+                class_tokens_mask = [True if class_token_index <= i < class_token_index+num_id_images else False \
+                     for i in range(len(clean_input_ids))]
+
+                clean_input_ids = torch.tensor(clean_input_ids, dtype=torch.long).unsqueeze(0)
+                class_tokens_mask = torch.tensor(class_tokens_mask, dtype=torch.bool).unsqueeze(0)
+
+                prompt_embeds = text_encoder(
+                    clean_input_ids.to(device),
+                    output_hidden_states=True,
+                )
+
+                # We are only ALWAYS interested in the pooled output of the final text encoder
+                pooled_prompt_embeds = prompt_embeds[0]
+                prompt_embeds = prompt_embeds.hidden_states[-2]
+                prompt_embeds_list.append(prompt_embeds)
+
+            prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
+
+        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
+        class_tokens_mask = class_tokens_mask.to(device=device) # TODO: ignoring two-prompt case
+
+        return prompt_embeds, pooled_prompt_embeds, class_tokens_mask
+
+    @property
+    def interrupt(self):
+        return self._interrupt
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        prompt_2: Optional[Union[str, List[str]]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        denoising_end: Optional[float] = None,
+        guidance_scale: float = 5.0,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        negative_prompt_2: 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,
+        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        guidance_rescale: float = 0.0,
+        original_size: Optional[Tuple[int, int]] = None,
+        crops_coords_top_left: Tuple[int, int] = (0, 0),
+        target_size: Optional[Tuple[int, int]] = None,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        # Added parameters (for PhotoMaker)
+        input_id_images: PipelineImageInput = None,
+        start_merge_step: int = 0, # TODO: change to `style_strength_ratio` in the future
+        class_tokens_mask: Optional[torch.LongTensor] = None,
+        prompt_embeds_text_only: Optional[torch.FloatTensor] = None,
+        pooled_prompt_embeds_text_only: Optional[torch.FloatTensor] = None,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+        Only the parameters introduced by PhotoMaker are discussed here.
+        For explanations of the previous parameters in StableDiffusionXLPipeline, please refer to https://github.com/huggingface/diffusers/blob/v0.25.0/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py
+        Args:
+            input_id_images (`PipelineImageInput`, *optional*):
+                Input ID Image to work with PhotoMaker.
+            class_tokens_mask (`torch.LongTensor`, *optional*):
+                Pre-generated class token. When the `prompt_embeds` parameter is provided in advance, it is necessary to prepare the `class_tokens_mask` beforehand for marking out the position of class word.
+            prompt_embeds_text_only (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            pooled_prompt_embeds_text_only (`torch.FloatTensor`, *optional*):
+                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+                If not provided, pooled text embeddings will be generated from `prompt` input argument.
+        Returns:
+            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
+            `tuple`. When returning a tuple, the first element is a list with the generated images.
+        """
+        # 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,
+            prompt_2,
+            height,
+            width,
+            callback_steps,
+            negative_prompt,
+            negative_prompt_2,
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+            callback_on_step_end_tensor_inputs,
+        )
+
+        self._interrupt = False
+
+        #
+        if prompt_embeds is not None and class_tokens_mask is None:
+            raise ValueError(
+                "If `prompt_embeds` are provided, `class_tokens_mask` also have to be passed. Make sure to generate `class_tokens_mask` from the same tokenizer that was used to generate `prompt_embeds`."
+            )
+        # check the input id images
+        if input_id_images is None:
+            raise ValueError(
+                "Provide `input_id_images`. Cannot leave `input_id_images` undefined for PhotoMaker pipeline."
+            )
+        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
+            prompt = [prompt]
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale >= 1.0
+
+        assert do_classifier_free_guidance
+
+        # 3. Encode input prompt
+        num_id_images = len(input_id_images)
+        if isinstance(prompt, list):
+            prompt_arr = prompt
+            negative_prompt_embeds_arr = []
+            prompt_embeds_text_only_arr = []
+            prompt_embeds_arr = []
+            latents_arr = []
+            add_time_ids_arr = []
+            negative_pooled_prompt_embeds_arr = []
+            pooled_prompt_embeds_text_only_arr = []
+            pooled_prompt_embeds_arr = []
+            for prompt in prompt_arr:
+                (
+                    prompt_embeds,
+                    pooled_prompt_embeds,
+                    class_tokens_mask,
+                ) = self.encode_prompt_with_trigger_word(
+                    prompt=prompt,
+                    prompt_2=prompt_2,
+                    device=device,
+                    num_id_images=num_id_images,
+                    prompt_embeds=prompt_embeds,
+                    pooled_prompt_embeds=pooled_prompt_embeds,
+                    class_tokens_mask=class_tokens_mask,
+                )
+
+                # 4. Encode input prompt without the trigger word for delayed conditioning
+                # encode, remove trigger word token, then decode
+                tokens_text_only = self.tokenizer.encode(prompt, add_special_tokens=False)
+                trigger_word_token = self.tokenizer.convert_tokens_to_ids(self.trigger_word)
+                tokens_text_only.remove(trigger_word_token)
+                prompt_text_only = self.tokenizer.decode(tokens_text_only, add_special_tokens=False)
+                print(prompt_text_only)
+                (
+                    prompt_embeds_text_only,
+                    negative_prompt_embeds,
+                    pooled_prompt_embeds_text_only, # TODO: replace the pooled_prompt_embeds with text only prompt
+                    negative_pooled_prompt_embeds,
+                ) = self.encode_prompt(
+                    prompt=prompt_text_only,
+                    prompt_2=prompt_2,
+                    device=device,
+                    num_images_per_prompt=num_images_per_prompt,
+                    do_classifier_free_guidance=True,
+                    negative_prompt=negative_prompt,
+                    negative_prompt_2=negative_prompt_2,
+                    prompt_embeds=prompt_embeds_text_only,
+                    negative_prompt_embeds=negative_prompt_embeds,
+                    pooled_prompt_embeds=pooled_prompt_embeds_text_only,
+                    negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+                )
+
+                # 5. Prepare the input ID images
+                dtype = next(self.id_encoder.parameters()).dtype
+                if not isinstance(input_id_images[0], torch.Tensor):
+                    id_pixel_values = self.id_image_processor(input_id_images, return_tensors="pt").pixel_values
+
+                id_pixel_values = id_pixel_values.unsqueeze(0).to(device=device, dtype=dtype) # TODO: multiple prompts
+
+                # 6. Get the update text embedding with the stacked ID embedding
+                prompt_embeds = self.id_encoder(id_pixel_values, prompt_embeds, class_tokens_mask)
+
+                bs_embed, seq_len, _ = prompt_embeds.shape
+                # duplicate text embeddings for each generation per prompt, using mps friendly method
+                prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+                prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+                pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
+                    bs_embed * num_images_per_prompt, -1
+                )
+
+
+                negative_prompt_embeds_arr.append(negative_prompt_embeds)
+                negative_prompt_embeds = None
+                negative_pooled_prompt_embeds_arr.append(negative_pooled_prompt_embeds)
+                negative_pooled_prompt_embeds = None
+                prompt_embeds_text_only_arr.append(prompt_embeds_text_only)
+                prompt_embeds_text_only = None
+                prompt_embeds_arr.append(prompt_embeds)
+                prompt_embeds = None
+                pooled_prompt_embeds_arr.append(pooled_prompt_embeds)
+                pooled_prompt_embeds = None
+                pooled_prompt_embeds_text_only_arr.append(pooled_prompt_embeds_text_only)
+                pooled_prompt_embeds_text_only = None
+            # 7. Prepare timesteps
+            self.scheduler.set_timesteps(num_inference_steps, device=device)
+            timesteps = self.scheduler.timesteps
+
+            negative_prompt_embeds = torch.cat(negative_prompt_embeds_arr ,dim =0)
+            print(negative_prompt_embeds.shape)
+            prompt_embeds = torch.cat(prompt_embeds_arr ,dim = 0)
+            print(prompt_embeds.shape)
+
+            prompt_embeds_text_only = torch.cat(prompt_embeds_text_only_arr   ,dim = 0)
+            print(prompt_embeds_text_only.shape)
+            pooled_prompt_embeds_text_only = torch.cat(pooled_prompt_embeds_text_only_arr  ,dim = 0)
+            print(pooled_prompt_embeds_text_only.shape)
+
+            negative_pooled_prompt_embeds = torch.cat(negative_pooled_prompt_embeds_arr ,dim = 0)
+            print(negative_pooled_prompt_embeds.shape)
+            pooled_prompt_embeds = torch.cat(pooled_prompt_embeds_arr,dim = 0)
+            print(pooled_prompt_embeds.shape)
+            # 8. Prepare latent variables
+            num_channels_latents = self.unet.config.in_channels
+            latents = self.prepare_latents(
+                batch_size * num_images_per_prompt,
+                num_channels_latents,
+                height,
+                width,
+                prompt_embeds.dtype,
+                device,
+                generator,
+                latents,
+            )
+
+            # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+            extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+            # 10. Prepare added time ids & embeddings
+            if self.text_encoder_2 is None:
+                text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
+            else:
+                text_encoder_projection_dim = self.text_encoder_2.config.projection_dim
+
+            add_time_ids = self._get_add_time_ids(
+                original_size,
+                crops_coords_top_left,
+                target_size,
+                dtype=prompt_embeds.dtype,
+                text_encoder_projection_dim=text_encoder_projection_dim,
+            )
+            add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0)
+            add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
+
+
+            print(latents.shape)
+            print(add_time_ids.shape)
+
+        # 11. 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):
+                if self.interrupt:
+                    continue
+
+                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(
+                        [negative_prompt_embeds, prompt_embeds_text_only], dim=0
+                    )
+                    add_text_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds_text_only], dim=0)
+                else:
+                    current_prompt_embeds = torch.cat(
+                        [negative_prompt_embeds, prompt_embeds], dim=0
+                    )
+                    add_text_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0)
+                # predict the noise residual
+                added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
+                # print(latent_model_input.shape)
+                # print(t)
+                # print(current_prompt_embeds.shape)
+                # print(add_text_embeds.shape)
+                # print(add_time_ids.shape)
+                #zeros_matrix =
+                #global_mask1024 = torch.cat([torch.randn(1, 1024, 1, 1, device=device) for random_number])
+                #global_mask4096 =
+                noise_pred = self.unet(
+                    latent_model_input,
+                    t,
+                    encoder_hidden_states=current_prompt_embeds,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    added_cond_kwargs=added_cond_kwargs,
+                    return_dict=False,
+                )[0]
+                # print(noise_pred.shape)
+                # 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)
+
+                if do_classifier_free_guidance and guidance_rescale > 0.0:
+                    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+                    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+
+                    ck_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+                    add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds)
+                    # negative_pooled_prompt_embeds = callback_outputs.pop(
+                    #     "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
+                    # )
+                    # add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids)
+                    # negative_add_time_ids = callback_outputs.pop("negative_add_time_ids", negative_add_time_ids)
+
+                # 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:
+                        step_idx = i // getattr(self.scheduler, "order", 1)
+                        callback(step_idx, t, latents)
+
+        # make sure the VAE is in float32 mode, as it overflows in float16
+        if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:
+            self.upcast_vae()
+            latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
+
+        if not output_type == "latent":
+            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+        else:
+            image = latents
+            return StableDiffusionXLPipelineOutput(images=image)
+
+        # apply watermark if available
+        # if self.watermark is not None:
+        #     image = self.watermark.apply_watermark(image)
+
+        image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return StableDiffusionXLPipelineOutput(images=image)
+pipe2 = PhotoMakerStableDiffusionXLPipeline.from_pretrained(
     models_dict["Unstable"], torch_dtype=torch.float16, use_safetensors=use_safetensors)
 pipe2 = pipe2.to("cpu")
 pipe2.load_photomaker_adapter(