v0.28.2/pipeline_fabric.py

# Copyright 2024 FABRIC authors and the HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List, Optional, Union

import torch
from packaging import version
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, UNet2DConditionModel
from diffusers.configuration_utils import FrozenDict
from diffusers.image_processor import VaeImageProcessor
from diffusers.loaders import LoraLoaderMixin, TextualInversionLoaderMixin
from diffusers.models.attention import BasicTransformerBlock
from diffusers.models.attention_processor import LoRAAttnProcessor
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.schedulers import EulerAncestralDiscreteScheduler, KarrasDiffusionSchedulers
from diffusers.utils import (
    deprecate,
    logging,
    replace_example_docstring,
)
from diffusers.utils.torch_utils import randn_tensor


logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

EXAMPLE_DOC_STRING = """
    Examples:
        ```py
        >>> from diffusers import DiffusionPipeline
        >>> import torch

        >>> model_id = "dreamlike-art/dreamlike-photoreal-2.0"
        >>> pipe = DiffusionPipeline(model_id, torch_dtype=torch.float16, custom_pipeline="pipeline_fabric")
        >>> pipe = pipe.to("cuda")
        >>> prompt = "a giant standing in a fantasy landscape best quality"
        >>> liked = []  # list of images for positive feedback
        >>> disliked = []  # list of images for negative feedback
        >>> image = pipe(prompt, num_images=4, liked=liked, disliked=disliked).images[0]
        ```
"""


class FabricCrossAttnProcessor:
    def __init__(self):
        self.attntion_probs = None

    def __call__(
        self,
        attn,
        hidden_states,
        encoder_hidden_states=None,
        attention_mask=None,
        weights=None,
        lora_scale=1.0,
    ):
        batch_size, sequence_length, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )
        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)

        if isinstance(attn.processor, LoRAAttnProcessor):
            query = attn.to_q(hidden_states) + lora_scale * attn.processor.to_q_lora(hidden_states)
        else:
            query = attn.to_q(hidden_states)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.norm_cross:
            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        if isinstance(attn.processor, LoRAAttnProcessor):
            key = attn.to_k(encoder_hidden_states) + lora_scale * attn.processor.to_k_lora(encoder_hidden_states)
            value = attn.to_v(encoder_hidden_states) + lora_scale * attn.processor.to_v_lora(encoder_hidden_states)
        else:
            key = attn.to_k(encoder_hidden_states)
            value = attn.to_v(encoder_hidden_states)

        query = attn.head_to_batch_dim(query)
        key = attn.head_to_batch_dim(key)
        value = attn.head_to_batch_dim(value)

        attention_probs = attn.get_attention_scores(query, key, attention_mask)

        if weights is not None:
            if weights.shape[0] != 1:
                weights = weights.repeat_interleave(attn.heads, dim=0)
            attention_probs = attention_probs * weights[:, None]
            attention_probs = attention_probs / attention_probs.sum(dim=-1, keepdim=True)

        hidden_states = torch.bmm(attention_probs, value)
        hidden_states = attn.batch_to_head_dim(hidden_states)

        # linear proj
        if isinstance(attn.processor, LoRAAttnProcessor):
            hidden_states = attn.to_out[0](hidden_states) + lora_scale * attn.processor.to_out_lora(hidden_states)
        else:
            hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        return hidden_states


class FabricPipeline(DiffusionPipeline):
    r"""
    Pipeline for text-to-image generation using Stable Diffusion and conditioning the results using feedback images.
    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
    implemented for all pipelines (downloading, saving, running on a particular device, etc.).

    Args:
        vae ([`AutoencoderKL`]):
            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
        text_encoder ([`~transformers.CLIPTextModel`]):
            Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
        tokenizer ([`~transformers.CLIPTokenizer`]):
            A `CLIPTokenizer` to tokenize text.
        unet ([`UNet2DConditionModel`]):
            A `UNet2DConditionModel` to denoise the encoded image latents.
        scheduler ([`EulerAncestralDiscreteScheduler`]):
            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
            about a model's potential harms.
    """

    def __init__(
        self,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        tokenizer: CLIPTokenizer,
        unet: UNet2DConditionModel,
        scheduler: KarrasDiffusionSchedulers,
        requires_safety_checker: bool = True,
    ):
        super().__init__()

        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
            version.parse(unet.config._diffusers_version).base_version
        ) < version.parse("0.9.0.dev0")
        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
            deprecation_message = (
                "The configuration file of the unet has set the default `sample_size` to smaller than"
                " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                " in the config might lead to incorrect results in future versions. If you have downloaded this"
                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
                " the `unet/config.json` file"
            )

            deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
            new_config = dict(unet.config)
            new_config["sample_size"] = 64
            unet._internal_dict = FrozenDict(new_config)

        self.register_modules(
            unet=unet,
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            scheduler=scheduler,
        )
        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
    def _encode_prompt(
        self,
        prompt,
        device,
        num_images_per_prompt,
        do_classifier_free_guidance,
        negative_prompt=None,
        prompt_embeds: Optional[torch.Tensor] = None,
        negative_prompt_embeds: Optional[torch.Tensor] = None,
        lora_scale: Optional[float] = None,
    ):
        r"""
        Encodes the prompt into text encoder hidden states.

        Args:
             prompt (`str` or `List[str]`, *optional*):
                prompt to be encoded
            device: (`torch.device`):
                torch device
            num_images_per_prompt (`int`):
                number of images that should be generated per prompt
            do_classifier_free_guidance (`bool`):
                whether to use classifier free guidance or not
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation. If not defined, one has to pass
                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
                less than `1`).
            prompt_embeds (`torch.Tensor`, *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.
            negative_prompt_embeds (`torch.Tensor`, *optional*):
                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
                argument.
            lora_scale (`float`, *optional*):
                A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
        """
        # set lora scale so that monkey patched LoRA
        # function of text encoder can correctly access it
        if lora_scale is not None and isinstance(self, LoraLoaderMixin):
            self._lora_scale = lora_scale

        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]

        if prompt_embeds is None:
            # textual inversion: process multi-vector tokens if necessary
            if isinstance(self, TextualInversionLoaderMixin):
                prompt = self.maybe_convert_prompt(prompt, self.tokenizer)

            text_inputs = self.tokenizer(
                prompt,
                padding="max_length",
                max_length=self.tokenizer.model_max_length,
                truncation=True,
                return_tensors="pt",
            )
            text_input_ids = text_inputs.input_ids
            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids

            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
                text_input_ids, untruncated_ids
            ):
                removed_text = self.tokenizer.batch_decode(
                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
                )
                logger.warning(
                    "The following part of your input was truncated because CLIP can only handle sequences up to"
                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
                )

            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
                attention_mask = text_inputs.attention_mask.to(device)
            else:
                attention_mask = None

            prompt_embeds = self.text_encoder(
                text_input_ids.to(device),
                attention_mask=attention_mask,
            )
            prompt_embeds = prompt_embeds[0]

        if self.text_encoder is not None:
            prompt_embeds_dtype = self.text_encoder.dtype
        elif self.unet is not None:
            prompt_embeds_dtype = self.unet.dtype
        else:
            prompt_embeds_dtype = prompt_embeds.dtype

        prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)

        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)

        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance and negative_prompt_embeds is None:
            uncond_tokens: List[str]
            if negative_prompt is None:
                uncond_tokens = [""] * batch_size
            elif prompt is not None and type(prompt) is not type(negative_prompt):
                raise TypeError(
                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
                    f" {type(prompt)}."
                )
            elif isinstance(negative_prompt, str):
                uncond_tokens = [negative_prompt]
            elif batch_size != len(negative_prompt):
                raise ValueError(
                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                    " the batch size of `prompt`."
                )
            else:
                uncond_tokens = negative_prompt

            # textual inversion: process multi-vector tokens if necessary
            if isinstance(self, TextualInversionLoaderMixin):
                uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)

            max_length = prompt_embeds.shape[1]
            uncond_input = self.tokenizer(
                uncond_tokens,
                padding="max_length",
                max_length=max_length,
                truncation=True,
                return_tensors="pt",
            )

            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
                attention_mask = uncond_input.attention_mask.to(device)
            else:
                attention_mask = None

            negative_prompt_embeds = self.text_encoder(
                uncond_input.input_ids.to(device),
                attention_mask=attention_mask,
            )
            negative_prompt_embeds = negative_prompt_embeds[0]

        if do_classifier_free_guidance:
            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
            seq_len = negative_prompt_embeds.shape[1]

            negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)

            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)

            # For classifier free guidance, we need to do two forward passes.
            # Here we concatenate the unconditional and text embeddings into a single batch
            # to avoid doing two forward passes
            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])

        return prompt_embeds

    def get_unet_hidden_states(self, z_all, t, prompt_embd):
        cached_hidden_states = []
        for module in self.unet.modules():
            if isinstance(module, BasicTransformerBlock):

                def new_forward(self, hidden_states, *args, **kwargs):
                    cached_hidden_states.append(hidden_states.clone().detach().cpu())
                    return self.old_forward(hidden_states, *args, **kwargs)

                module.attn1.old_forward = module.attn1.forward
                module.attn1.forward = new_forward.__get__(module.attn1)

        # run forward pass to cache hidden states, output can be discarded
        _ = self.unet(z_all, t, encoder_hidden_states=prompt_embd)

        # restore original forward pass
        for module in self.unet.modules():
            if isinstance(module, BasicTransformerBlock):
                module.attn1.forward = module.attn1.old_forward
                del module.attn1.old_forward

        return cached_hidden_states

    def unet_forward_with_cached_hidden_states(
        self,
        z_all,
        t,
        prompt_embd,
        cached_pos_hiddens: Optional[List[torch.Tensor]] = None,
        cached_neg_hiddens: Optional[List[torch.Tensor]] = None,
        pos_weights=(0.8, 0.8),
        neg_weights=(0.5, 0.5),
    ):
        if cached_pos_hiddens is None and cached_neg_hiddens is None:
            return self.unet(z_all, t, encoder_hidden_states=prompt_embd)

        local_pos_weights = torch.linspace(*pos_weights, steps=len(self.unet.down_blocks) + 1)[:-1].tolist()
        local_neg_weights = torch.linspace(*neg_weights, steps=len(self.unet.down_blocks) + 1)[:-1].tolist()
        for block, pos_weight, neg_weight in zip(
            self.unet.down_blocks + [self.unet.mid_block] + self.unet.up_blocks,
            local_pos_weights + [pos_weights[1]] + local_pos_weights[::-1],
            local_neg_weights + [neg_weights[1]] + local_neg_weights[::-1],
        ):
            for module in block.modules():
                if isinstance(module, BasicTransformerBlock):

                    def new_forward(
                        self,
                        hidden_states,
                        pos_weight=pos_weight,
                        neg_weight=neg_weight,
                        **kwargs,
                    ):
                        cond_hiddens, uncond_hiddens = hidden_states.chunk(2, dim=0)
                        batch_size, d_model = cond_hiddens.shape[:2]
                        device, dtype = hidden_states.device, hidden_states.dtype

                        weights = torch.ones(batch_size, d_model, device=device, dtype=dtype)
                        out_pos = self.old_forward(hidden_states)
                        out_neg = self.old_forward(hidden_states)

                        if cached_pos_hiddens is not None:
                            cached_pos_hs = cached_pos_hiddens.pop(0).to(hidden_states.device)
                            cond_pos_hs = torch.cat([cond_hiddens, cached_pos_hs], dim=1)
                            pos_weights = weights.clone().repeat(1, 1 + cached_pos_hs.shape[1] // d_model)
                            pos_weights[:, d_model:] = pos_weight
                            attn_with_weights = FabricCrossAttnProcessor()
                            out_pos = attn_with_weights(
                                self,
                                cond_hiddens,
                                encoder_hidden_states=cond_pos_hs,
                                weights=pos_weights,
                            )
                        else:
                            out_pos = self.old_forward(cond_hiddens)

                        if cached_neg_hiddens is not None:
                            cached_neg_hs = cached_neg_hiddens.pop(0).to(hidden_states.device)
                            uncond_neg_hs = torch.cat([uncond_hiddens, cached_neg_hs], dim=1)
                            neg_weights = weights.clone().repeat(1, 1 + cached_neg_hs.shape[1] // d_model)
                            neg_weights[:, d_model:] = neg_weight
                            attn_with_weights = FabricCrossAttnProcessor()
                            out_neg = attn_with_weights(
                                self,
                                uncond_hiddens,
                                encoder_hidden_states=uncond_neg_hs,
                                weights=neg_weights,
                            )
                        else:
                            out_neg = self.old_forward(uncond_hiddens)

                        out = torch.cat([out_pos, out_neg], dim=0)
                        return out

                    module.attn1.old_forward = module.attn1.forward
                    module.attn1.forward = new_forward.__get__(module.attn1)

        out = self.unet(z_all, t, encoder_hidden_states=prompt_embd)

        # restore original forward pass
        for module in self.unet.modules():
            if isinstance(module, BasicTransformerBlock):
                module.attn1.forward = module.attn1.old_forward
                del module.attn1.old_forward

        return out

    def preprocess_feedback_images(self, images, vae, dim, device, dtype, generator) -> torch.tensor:
        images_t = [self.image_to_tensor(img, dim, dtype) for img in images]
        images_t = torch.stack(images_t).to(device)
        latents = vae.config.scaling_factor * vae.encode(images_t).latent_dist.sample(generator)

        return torch.cat([latents], dim=0)

    def check_inputs(
        self,
        prompt,
        negative_prompt=None,
        liked=None,
        disliked=None,
        height=None,
        width=None,
    ):
        if prompt is None:
            raise ValueError("Provide `prompt`. Cannot leave both `prompt` undefined.")
        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")

        if negative_prompt is not None and (
            not isinstance(negative_prompt, str) and not isinstance(negative_prompt, list)
        ):
            raise ValueError(f"`negative_prompt` has to be of type `str` or `list` but is {type(negative_prompt)}")

        if liked is not None and not isinstance(liked, list):
            raise ValueError(f"`liked` has to be of type `list` but is {type(liked)}")

        if disliked is not None and not isinstance(disliked, list):
            raise ValueError(f"`disliked` has to be of type `list` but is {type(disliked)}")

        if height is not None and not isinstance(height, int):
            raise ValueError(f"`height` has to be of type `int` but is {type(height)}")

        if width is not None and not isinstance(width, int):
            raise ValueError(f"`width` has to be of type `int` but is {type(width)}")

    @torch.no_grad()
    @replace_example_docstring(EXAMPLE_DOC_STRING)
    def __call__(
        self,
        prompt: Optional[Union[str, List[str]]] = "",
        negative_prompt: Optional[Union[str, List[str]]] = "lowres, bad anatomy, bad hands, cropped, worst quality",
        liked: Optional[Union[List[str], List[Image.Image]]] = [],
        disliked: Optional[Union[List[str], List[Image.Image]]] = [],
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        height: int = 512,
        width: int = 512,
        return_dict: bool = True,
        num_images: int = 4,
        guidance_scale: float = 7.0,
        num_inference_steps: int = 20,
        output_type: Optional[str] = "pil",
        feedback_start_ratio: float = 0.33,
        feedback_end_ratio: float = 0.66,
        min_weight: float = 0.05,
        max_weight: float = 0.8,
        neg_scale: float = 0.5,
        pos_bottleneck_scale: float = 1.0,
        neg_bottleneck_scale: float = 1.0,
        latents: Optional[torch.Tensor] = None,
    ):
        r"""
        The call function to the pipeline for generation. Generate a trajectory of images with binary feedback. The
        feedback can be given as a list of liked and disliked images.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`
                instead.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
            liked (`List[Image.Image]` or `List[str]`, *optional*):
                Encourages images with liked features.
            disliked (`List[Image.Image]` or `List[str]`, *optional*):
                Discourages images with disliked features.
            generator (`torch.Generator` or `List[torch.Generator]` or `int`, *optional*):
                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) or an `int` to
                make generation deterministic.
            height (`int`, *optional*, defaults to 512):
                Height of the generated image.
            width (`int`, *optional*, defaults to 512):
                Width of the generated image.
            num_images (`int`, *optional*, defaults to 4):
                The number of images to generate per prompt.
            guidance_scale (`float`, *optional*, defaults to 7.0):
                A higher guidance scale value encourages the model to generate images closely linked to the text
                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
            num_inference_steps (`int`, *optional*, defaults to 20):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
                plain tuple.
            feedback_start_ratio (`float`, *optional*, defaults to `.33`):
                Start point for providing feedback (between 0 and 1).
            feedback_end_ratio (`float`, *optional*, defaults to `.66`):
                End point for providing feedback (between 0 and 1).
            min_weight (`float`, *optional*, defaults to `.05`):
                Minimum weight for feedback.
            max_weight (`float`, *optional*, defults tp `1.0`):
                Maximum weight for feedback.
            neg_scale (`float`, *optional*, defaults to `.5`):
                Scale factor for negative feedback.

        Examples:

        Returns:
            [`~pipelines.fabric.FabricPipelineOutput`] or `tuple`:
                If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
                otherwise a `tuple` is returned where the first element is a list with the generated images and the
                second element is a list of `bool`s indicating whether the corresponding generated image contains
                "not-safe-for-work" (nsfw) content.

        """

        self.check_inputs(prompt, negative_prompt, liked, disliked)

        device = self._execution_device
        dtype = self.unet.dtype

        if isinstance(prompt, str) and prompt is not None:
            batch_size = 1
        elif isinstance(prompt, list) and prompt is not None:
            batch_size = len(prompt)
        else:
            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")

        if isinstance(negative_prompt, str):
            negative_prompt = negative_prompt
        elif isinstance(negative_prompt, list):
            negative_prompt = negative_prompt
        else:
            assert len(negative_prompt) == batch_size

        shape = (
            batch_size * num_images,
            self.unet.config.in_channels,
            height // self.vae_scale_factor,
            width // self.vae_scale_factor,
        )
        latent_noise = randn_tensor(
            shape,
            device=device,
            dtype=dtype,
            generator=generator,
        )

        positive_latents = (
            self.preprocess_feedback_images(liked, self.vae, (height, width), device, dtype, generator)
            if liked and len(liked) > 0
            else torch.tensor(
                [],
                device=device,
                dtype=dtype,
            )
        )
        negative_latents = (
            self.preprocess_feedback_images(disliked, self.vae, (height, width), device, dtype, generator)
            if disliked and len(disliked) > 0
            else torch.tensor(
                [],
                device=device,
                dtype=dtype,
            )
        )

        do_classifier_free_guidance = guidance_scale > 0.1

        (prompt_neg_embs, prompt_pos_embs) = self._encode_prompt(
            prompt,
            device,
            num_images,
            do_classifier_free_guidance,
            negative_prompt,
        ).split([num_images * batch_size, num_images * batch_size])

        batched_prompt_embd = torch.cat([prompt_pos_embs, prompt_neg_embs], dim=0)

        null_tokens = self.tokenizer(
            [""],
            return_tensors="pt",
            max_length=self.tokenizer.model_max_length,
            padding="max_length",
            truncation=True,
        )

        if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
            attention_mask = null_tokens.attention_mask.to(device)
        else:
            attention_mask = None

        null_prompt_emb = self.text_encoder(
            input_ids=null_tokens.input_ids.to(device),
            attention_mask=attention_mask,
        ).last_hidden_state

        null_prompt_emb = null_prompt_emb.to(device=device, dtype=dtype)

        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps = self.scheduler.timesteps
        latent_noise = latent_noise * self.scheduler.init_noise_sigma

        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order

        ref_start_idx = round(len(timesteps) * feedback_start_ratio)
        ref_end_idx = round(len(timesteps) * feedback_end_ratio)

        with self.progress_bar(total=num_inference_steps) as pbar:
            for i, t in enumerate(timesteps):
                sigma = self.scheduler.sigma_t[t] if hasattr(self.scheduler, "sigma_t") else 0
                if hasattr(self.scheduler, "sigmas"):
                    sigma = self.scheduler.sigmas[i]

                alpha_hat = 1 / (sigma**2 + 1)

                z_single = self.scheduler.scale_model_input(latent_noise, t)
                z_all = torch.cat([z_single] * 2, dim=0)
                z_ref = torch.cat([positive_latents, negative_latents], dim=0)

                if i >= ref_start_idx and i <= ref_end_idx:
                    weight_factor = max_weight
                else:
                    weight_factor = min_weight

                pos_ws = (weight_factor, weight_factor * pos_bottleneck_scale)
                neg_ws = (weight_factor * neg_scale, weight_factor * neg_scale * neg_bottleneck_scale)

                if z_ref.size(0) > 0 and weight_factor > 0:
                    noise = torch.randn_like(z_ref)
                    if isinstance(self.scheduler, EulerAncestralDiscreteScheduler):
                        z_ref_noised = (alpha_hat**0.5 * z_ref + (1 - alpha_hat) ** 0.5 * noise).type(dtype)
                    else:
                        z_ref_noised = self.scheduler.add_noise(z_ref, noise, t)

                    ref_prompt_embd = torch.cat(
                        [null_prompt_emb] * (len(positive_latents) + len(negative_latents)), dim=0
                    )
                    cached_hidden_states = self.get_unet_hidden_states(z_ref_noised, t, ref_prompt_embd)

                    n_pos, n_neg = positive_latents.shape[0], negative_latents.shape[0]
                    cached_pos_hs, cached_neg_hs = [], []
                    for hs in cached_hidden_states:
                        cached_pos, cached_neg = hs.split([n_pos, n_neg], dim=0)
                        cached_pos = cached_pos.view(1, -1, *cached_pos.shape[2:]).expand(num_images, -1, -1)
                        cached_neg = cached_neg.view(1, -1, *cached_neg.shape[2:]).expand(num_images, -1, -1)
                        cached_pos_hs.append(cached_pos)
                        cached_neg_hs.append(cached_neg)

                    if n_pos == 0:
                        cached_pos_hs = None
                    if n_neg == 0:
                        cached_neg_hs = None
                else:
                    cached_pos_hs, cached_neg_hs = None, None
                unet_out = self.unet_forward_with_cached_hidden_states(
                    z_all,
                    t,
                    prompt_embd=batched_prompt_embd,
                    cached_pos_hiddens=cached_pos_hs,
                    cached_neg_hiddens=cached_neg_hs,
                    pos_weights=pos_ws,
                    neg_weights=neg_ws,
                )[0]

                noise_cond, noise_uncond = unet_out.chunk(2)
                guidance = noise_cond - noise_uncond
                noise_pred = noise_uncond + guidance_scale * guidance
                latent_noise = self.scheduler.step(noise_pred, t, latent_noise)[0]

                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                    pbar.update()

        y = self.vae.decode(latent_noise / self.vae.config.scaling_factor, return_dict=False)[0]
        imgs = self.image_processor.postprocess(
            y,
            output_type=output_type,
        )

        if not return_dict:
            return imgs

        return StableDiffusionPipelineOutput(imgs, False)

    def image_to_tensor(self, image: Union[str, Image.Image], dim: tuple, dtype):
        """
        Convert latent PIL image to a torch tensor for further processing.
        """
        if isinstance(image, str):
            image = Image.open(image)
        if not image.mode == "RGB":
            image = image.convert("RGB")
        image = self.image_processor.preprocess(image, height=dim[0], width=dim[1])[0]
        return image.type(dtype)