bardofcodes
/

pattern_analogies

@@ -1,741 +0,0 @@
-"""
-ADOBE CONFIDENTIAL
-Copyright 2024 Adobe
-All Rights Reserved.
-NOTICE: All information contained herein is, and remains
-the property of Adobe and its suppliers, if any. The intellectual
-and technical concepts contained herein are proprietary to Adobe
-and its suppliers and are protected by all applicable intellectual
-property laws, including trade secret and copyright laws.
-Dissemination of this information or reproduction of this material
-is strictly forbidden unless prior written permission is obtained
-from Adobe.
-"""
-from typing import Callable, List, Optional, Union
-import inspect
-import einops
-import PIL.Image
-import numpy as np
-import torch as th
-import torch.nn as nn
-from torchvision import transforms
-from diffusers import ModelMixin
-from transformers import AutoModel, AutoConfig, SiglipVisionConfig, Dinov2Config, Dinov2Model
-from transformers import SiglipVisionModel
-from diffusers import DiffusionPipeline
-from diffusers.image_processor import VaeImageProcessor
-from diffusers.models import AutoencoderKL, UNet2DConditionModel
-from diffusers.schedulers import KarrasDiffusionSchedulers
-from diffusers.utils.torch_utils import randn_tensor
-from diffusers.pipelines.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-# REf: https://github.com/tatp22/multidim-positional-encoding/tree/master
-OUT_SIZE = 768
-IN_SIZE = 2048
-DINO_SIZE = 224
-DINO_MEAN = [0.485, 0.456, 0.406]
-DINO_STD = [0.229, 0.224, 0.225]
-SIGLIP_SIZE = 256
-SIGLIP_MEAN = [0.5]
-SIGLIP_STD = [0.5]
-def get_emb(sin_inp):
-    """
-    Gets a base embedding for one dimension with sin and cos intertwined
-    """
-    emb = th.stack((sin_inp.sin(), sin_inp.cos()), dim=-1)
-    return th.flatten(emb, -2, -1)
-class PositionalEncoding1D(nn.Module):
-    def __init__(self, channels):
-        """
-        :param channels: The last dimension of the tensor you want to apply pos emb to.
-        """
-        super(PositionalEncoding1D, self).__init__()
-        self.org_channels = channels
-        channels = int(np.ceil(channels / 2) * 2)
-        self.channels = channels
-        inv_freq = 1.0 / (10000 ** (th.arange(0, channels, 2).float() / channels))
-        self.register_buffer("inv_freq", inv_freq)
-        self.register_buffer("cached_penc", None, persistent=False)
-    def forward(self, tensor):
-        """
-        :param tensor: A 3d tensor of size (batch_size, x, ch)
-        :return: Positional Encoding Matrix of size (batch_size, x, ch)
-        """
-        if len(tensor.shape) != 3:
-            raise RuntimeError("The input tensor has to be 3d!")
-        if self.cached_penc is not None and self.cached_penc.shape == tensor.shape:
-            return self.cached_penc
-        self.cached_penc = None
-        batch_size, x, orig_ch = tensor.shape
-        pos_x = th.arange(x, device=tensor.device, dtype=self.inv_freq.dtype)
-        sin_inp_x = th.einsum("i,j->ij", pos_x, self.inv_freq)
-        emb_x = get_emb(sin_inp_x)
-        emb = th.zeros((x, self.channels), device=tensor.device, dtype=tensor.dtype)
-        emb[:, : self.channels] = emb_x
-        self.cached_penc = emb[None, :, :orig_ch].repeat(batch_size, 1, 1)
-        return self.cached_penc
-class PositionalEncoding3D(nn.Module):
-    def __init__(self, channels):
-        """
-        :param channels: The last dimension of the tensor you want to apply pos emb to.
-        """
-        super(PositionalEncoding3D, self).__init__()
-        self.org_channels = channels
-        channels = int(np.ceil(channels / 6) * 2)
-        if channels % 2:
-            channels += 1
-        self.channels = channels
-        inv_freq = 1.0 / (10000 ** (th.arange(0, channels, 2).float() / channels))
-        self.register_buffer("inv_freq", inv_freq)
-        self.register_buffer("cached_penc", None, persistent=False)
-    def forward(self, tensor):
-        """
-        :param tensor: A 5d tensor of size (batch_size, x, y, z, ch)
-        :return: Positional Encoding Matrix of size (batch_size, x, y, z, ch)
-        """
-        if len(tensor.shape) != 5:
-            raise RuntimeError("The input tensor has to be 5d!")
-        if self.cached_penc is not None and self.cached_penc.shape == tensor.shape:
-            return self.cached_penc
-        self.cached_penc = None
-        batch_size, x, y, z, orig_ch = tensor.shape
-        pos_x = th.arange(x, device=tensor.device, dtype=self.inv_freq.dtype)
-        pos_y = th.arange(y, device=tensor.device, dtype=self.inv_freq.dtype)
-        pos_z = th.arange(z, device=tensor.device, dtype=self.inv_freq.dtype)
-        sin_inp_x = th.einsum("i,j->ij", pos_x, self.inv_freq)
-        sin_inp_y = th.einsum("i,j->ij", pos_y, self.inv_freq)
-        sin_inp_z = th.einsum("i,j->ij", pos_z, self.inv_freq)
-        emb_x = get_emb(sin_inp_x).unsqueeze(1).unsqueeze(1)
-        emb_y = get_emb(sin_inp_y).unsqueeze(1)
-        emb_z = get_emb(sin_inp_z)
-        emb = th.zeros(
-            (x, y, z, self.channels * 3),
-            device=tensor.device,
-            dtype=tensor.dtype,
-        )
-        emb[:, :, :, : self.channels] = emb_x
-        emb[:, :, :, self.channels : 2 * self.channels] = emb_y
-        emb[:, :, :, 2 * self.channels :] = emb_z
-        self.cached_penc = emb[None, :, :, :, :orig_ch].repeat(batch_size, 1, 1, 1, 1)
-        return self.cached_penc
-class AnalogyInputProcessor(ModelMixin, ConfigMixin):
-    @register_to_config
-    def __init__(self,):
-        super(AnalogyInputProcessor, self).__init__()
-        self.dino_transform = transforms.Compose(
-            [
-                transforms.Resize((DINO_SIZE, DINO_SIZE)),
-                transforms.ToTensor(),
-                transforms.Normalize(DINO_MEAN, DINO_STD), # SIGLIP normalization
-            ]
-        )
-        self.siglip_transform = transforms.Compose(
-            [
-                transforms.Resize((SIGLIP_SIZE, SIGLIP_SIZE)),
-                transforms.ToTensor(),
-                transforms.Normalize(SIGLIP_MEAN, SIGLIP_STD), # SIGLIP normalization
-            ]
-        )
-        dino_mean = th.tensor(DINO_MEAN).view(1, 3, 1, 1)
-        dino_std = th.tensor(DINO_STD).view(1, 3, 1, 1)
-        siglip_mean = [SIGLIP_MEAN[0],] * 3
-        siglip_std = [SIGLIP_STD[0],] * 3
-        siglip_mean = th.tensor(siglip_mean).view(1, 3, 1, 1)
-        siglip_std = th.tensor(siglip_std).view(1, 3, 1, 1)
-        self.register_buffer("dino_mean", dino_mean)
-        self.register_buffer("dino_std", dino_std)
-        self.register_buffer("siglip_mean", siglip_mean)
-        self.register_buffer("siglip_std", siglip_std)
-    def __call__(self, analogy_prompt):
-        # List of tuples of (A, A*, B)
-        img_a_dino = []
-        img_a_siglip = []
-        img_a_star_dino = []
-        img_a_star_siglip = []
-        img_b_dino = []
-        img_b_siglip = []
-        for im_set in analogy_prompt:
-            img_a, img_a_star, img_b = im_set
-            img_a_dino.append(self.dino_transform(img_a))
-            img_a_siglip.append(self.siglip_transform(img_a))
-            img_a_star_dino.append(self.dino_transform(img_a_star))
-            img_a_star_siglip.append(self.siglip_transform(img_a_star))
-            img_b_dino.append(self.dino_transform(img_b))
-            img_b_siglip.append(self.siglip_transform(img_b))
-        img_a_dino = th.stack(img_a_dino, 0)
-        img_a_siglip = th.stack(img_a_siglip, 0)
-        img_a_star_dino = th.stack(img_a_star_dino, 0)
-        img_a_star_siglip = th.stack(img_a_star_siglip, 0)
-        img_b_dino = th.stack(img_b_dino, 0)
-        img_b_siglip = th.stack(img_b_siglip, 0)
-        dino_combined_input = th.stack([img_b_dino, img_a_dino, img_a_star_dino], 0)
-        siglip_combined_input = th.stack([img_b_siglip, img_a_siglip, img_a_star_siglip], 0)
-        return dino_combined_input, siglip_combined_input
-    def get_negative(self, dino_in, siglip_in):
-        dino_i = ((dino_in * 0 + 0.5) - self.dino_mean) / self.dino_std
-        siglip_i = ((siglip_in * 0 + 0.5) - self.siglip_mean) / self.siglip_std
-        return dino_i, siglip_i
-class AnalogyProjector(ModelMixin, ConfigMixin):
-    @register_to_config
-    def __init__(self):
-        super(AnalogyProjector, self).__init__()
-        self.projector = DinoSiglipMixer()
-        self.pos_embd_1D = PositionalEncoding1D(OUT_SIZE)
-        self.pos_embd_3D = PositionalEncoding3D(OUT_SIZE)
-    def forward(self, dino_in, siglip_in, batch_size):
-        image_embeddings = self.projector(dino_in, siglip_in)
-        image_embeddings = einops.rearrange(image_embeddings, '(k b) t d -> b k t d', b=batch_size)
-        image_embeddings = self.position_embd(image_embeddings)
-        return image_embeddings
-    def position_embd(self, image_embeddings, concat=False):
-        canvas_embd = image_embeddings[:, :, 1:, :]
-        batch_size = canvas_embd.shape[0]
-        type_size = canvas_embd.shape[1]
-        xy_size = canvas_embd.shape[2]
-        x_size = int(xy_size ** 0.5)
-        canvas_embd = canvas_embd.reshape(batch_size, type_size, x_size, x_size, -1)
-        if concat:
-            canvas_embd = th.cat([canvas_embd, self.pos_embd_3D(canvas_embd)], -1)
-        else:
-            canvas_embd = self.pos_embd_3D(canvas_embd) + canvas_embd
-        canvas_embd = canvas_embd.reshape(batch_size, type_size, xy_size, -1)
-        class_embd = image_embeddings[:, :, 0, :]
-        if concat:
-            class_embd = th.cat([class_embd, self.pos_embd_1D(class_embd)], -1)
-        else:
-            class_embd = self.pos_embd_1D(class_embd) + class_embd
-        all_embd_list = []
-        for i in range(type_size):
-            all_embd_list.append(class_embd[:, i:i+1])
-            all_embd_list.append(canvas_embd[:, i])
-        image_embeddings = th.cat(all_embd_list, 1)
-        return image_embeddings
-class HighLowMixer(th.nn.Module):
-    def __init__(self, in_size=IN_SIZE, out_size=OUT_SIZE):
-        super().__init__()
-        mid_size = (in_size + out_size) // 2
-        self.lower_projector = th.nn.Sequential(
-            th.nn.LayerNorm(IN_SIZE//2),
-            th.nn.SiLU()
-        )
-        self.upper_projector = th.nn.Sequential(
-            th.nn.LayerNorm(IN_SIZE//2),
-            th.nn.SiLU()
-        )
-        self.projectors = th.nn.ModuleList([
-            # add layer norm
-            th.nn.Linear(in_size, mid_size),
-            th.nn.SiLU(),
-            th.nn.Linear(mid_size, out_size)
-        ])
-        # initialize
-        for proj in self.projectors:
-            if isinstance(proj, th.nn.Linear):
-                th.nn.init.xavier_uniform_(proj.weight)
-                th.nn.init.zeros_(proj.bias)
-    def forward(self, lower_in, upper_in, ):
-        # ALso format lower_in
-        lower_in = self.lower_projector(lower_in)
-        upper_in = self.upper_projector(upper_in)
-        x = th.cat([lower_in, upper_in], -1)
-        for proj in self.projectors:
-            x = proj(x)
-        return x
-class DinoSiglipMixer(th.nn.Module):
-    def __init__(self, in_size=OUT_SIZE * 2, out_size=OUT_SIZE):
-        super().__init__()
-        self.dino_projector = HighLowMixer()
-        self.siglip_projector = HighLowMixer()
-        self.projectors = th.nn.Sequential(
-            th.nn.SiLU(),
-            th.nn.Linear(in_size, out_size),
-        )
-        # initialize
-        for proj in self.projectors:
-            if isinstance(proj, th.nn.Linear):
-                th.nn.init.xavier_uniform_(proj.weight)
-                th.nn.init.zeros_(proj.bias)
-    def forward(self, dino_in, siglip_in):
-        # ALso format lower_in
-        lower, upper = th.chunk(dino_in, 2, -1)
-        dino_out = self.dino_projector(lower, upper)
-        lower, upper = th.chunk(siglip_in, 2, -1)
-        siglip_out = self.siglip_projector(lower, upper)
-        x = th.cat([dino_out, siglip_out], -1)
-        for proj in self.projectors:
-            x = proj(x)
-        return x
-class AnalogyEncoder(ModelMixin, ConfigMixin):
-    @register_to_config
-    def __init__(self, load_pretrained=False,
-                 dino_config_dict=None, siglip_config_dict=None):
-        super().__init__()
-        if load_pretrained:
-            image_encoder_dino = AutoModel.from_pretrained('facebook/dinov2-large', torch_dtype=th.float16)
-            image_encoder_siglip = SiglipVisionModel.from_pretrained("google/siglip-large-patch16-256", torch_dtype=th.float16, attn_implementation="sdpa")
-        else:
-            image_encoder_dino = AutoModel.from_config(Dinov2Config.from_dict(dino_config_dict))
-            image_encoder_siglip = AutoModel.from_config(SiglipVisionConfig.from_dict(siglip_config_dict))
-        image_encoder_dino.requires_grad_(False)
-        image_encoder_dino = image_encoder_dino.to(memory_format=th.channels_last)
-        image_encoder_siglip.requires_grad_(False)
-        image_encoder_siglip = image_encoder_siglip.to(memory_format=th.channels_last)
-        self.image_encoder_dino = image_encoder_dino
-        self.image_encoder_siglip = image_encoder_siglip
-    def dino_normalization(self, encoder_output):
-        embeds = encoder_output.last_hidden_state
-        embeds_pooled = embeds[:, 0:1]
-        embeds = embeds / th.norm(embeds_pooled, dim=-1, keepdim=True)
-        return embeds
-    def siglip_normalization(self, encoder_output):
-        embeds = th.cat ([encoder_output.pooler_output[:, None, :], encoder_output.last_hidden_state], dim=1)
-        embeds_pooled = embeds[:, 0:1]
-        embeds = embeds / th.norm(embeds_pooled, dim=-1, keepdim=True)
-        return embeds
-    def forward(self, dino_in, siglip_in):
-        x_1 = self.image_encoder_dino(dino_in, output_hidden_states=True)
-        x_1_first = x_1.hidden_states[0]
-        x_1 = self.dino_normalization(x_1)
-        x_2 = self.image_encoder_siglip(siglip_in, output_hidden_states=True)
-        x_2_first = x_2.hidden_states[0]
-        x_2_first_pool = th.mean(x_2_first, dim=1, keepdim=True)
-        x_2_first = th.cat([x_2_first_pool, x_2_first], 1)
-        x_2 = self.siglip_normalization(x_2)
-        dino_embd = th.cat([x_1, x_1_first], -1)
-        siglip_embd = th.cat([x_2, x_2_first], -1)
-        return dino_embd, siglip_embd
-class PatternAnalogyTrifuser(DiffusionPipeline):
-    r"""
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-    """
-    model_cpu_offload_seq = "bert->unet->vqvae"
-    analogy_input_processor: AnalogyInputProcessor
-    analogy_encoder: AnalogyEncoder
-    analogy_projector: AnalogyProjector
-    unet: UNet2DConditionModel
-    vae: AutoencoderKL
-    scheduler: KarrasDiffusionSchedulers
-    def __init__(self,
-        analogy_input_processor: AnalogyInputProcessor,
-        analogy_projector: AnalogyProjector,
-        analogy_encoder: AnalogyEncoder,
-        unet: UNet2DConditionModel,
-        vae: AutoencoderKL,
-        scheduler: KarrasDiffusionSchedulers,):
-        super().__init__()
-        self.register_modules(
-            analogy_input_processor=analogy_input_processor,
-            analogy_encoder=analogy_encoder,
-            analogy_projector=analogy_projector,
-            unet=unet,
-            vae=vae,
-            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_image_variation.StableDiffusionImageVariationPipeline.check_inputs
-    def check_inputs(self, analogy_prompt, negative_analogy_prompt, height, width, callback_steps):
-        if (
-            not isinstance(analogy_prompt, th.Tensor)
-            and not isinstance(analogy_prompt, PIL.Image.Image)
-            and not isinstance(analogy_prompt, list)
-        ):
-            raise ValueError(
-                "`analogy_prompt` contents have to be of type `torch.Tensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is"
-                f" {type(analogy_prompt)}"
-            )
-        if not negative_analogy_prompt is None:
-            if (
-                not isinstance(negative_analogy_prompt, th.Tensor)
-                and not isinstance(negative_analogy_prompt, PIL.Image.Image)
-                and not isinstance(negative_analogy_prompt, list)
-            ):
-                raise ValueError(
-                    "`negative_analogy_prompt` contents have to be of type `torch.Tensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is"
-                    f" {type(negative_analogy_prompt)}"
-                )
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
-        ):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
-    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
-        shape = (
-            batch_size,
-            num_channels_latents,
-            int(height) // self.vae_scale_factor,
-            int(width) // self.vae_scale_factor,
-        )
-        if isinstance(generator, list) and len(generator) != batch_size:
-            raise ValueError(
-                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-            )
-        if latents is None:
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        else:
-            latents = latents.to(device)
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
-        return latents
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
-    def prepare_extra_step_kwargs(self, generator, eta):
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-        # check if the scheduler accepts generator
-        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        if accepts_generator:
-            extra_step_kwargs["generator"] = generator
-        return extra_step_kwargs
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
-    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
-        shape = (
-            batch_size,
-            num_channels_latents,
-            int(height) // self.vae_scale_factor,
-            int(width) // self.vae_scale_factor,
-        )
-        if isinstance(generator, list) and len(generator) != batch_size:
-            raise ValueError(
-                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-            )
-        if latents is None:
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        else:
-            latents = latents.to(device)
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
-        return latents
-    def _encode_prompt(self, analogy_prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-        Args:
-            prompt (`str` or `List[str]`):
-                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]`):
-                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
-                if `guidance_scale` is less than `1`).
-        """
-        weight_dtype = self.unet.dtype
-        dino_input, siglip_input = self.analogy_input_processor(analogy_prompt)
-        dino_input = dino_input.to(device=device).to(dtype=weight_dtype)
-        siglip_input = siglip_input.to(device=device).to(dtype=weight_dtype)
-        batch_size = dino_input.shape[1]
-        dino_input_reshaped = einops.rearrange(dino_input, "k b c h w -> (k b) c h w")
-        siglip_input_reshaped = einops.rearrange(siglip_input, "k b c h w -> (k b) c h w")
-        dino_enc, siglip_enc = self.analogy_encoder(dino_input_reshaped, siglip_input_reshaped)
-        image_embeddings = self.analogy_projector(dino_enc, siglip_enc, batch_size)
-        # Check size here.
-        bs_embed, seq_len, _ = image_embeddings.shape
-        image_embeddings = image_embeddings.repeat(num_images_per_prompt, 1, 1)
-        # get unconditional embeddings for classifier free guidance
-        if do_classifier_free_guidance:
-            uncond_images: List[str]
-            if negative_prompt is None:
-                uncond_images = [np.zeros((512, 512, 3)) + 0.5] * batch_size
-            elif type(negative_prompt) is not type(analogy_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(analogy_prompt)} !="
-                    f" {type(negative_prompt)}."
-                )
-            elif isinstance(negative_prompt, PIL.Image.Image):
-                uncond_images = [negative_prompt]
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {analogy_prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_images = negative_prompt
-            dino_neg, siglip_neg = self.analogy_input_processor.get_negative(dino_input, siglip_input)
-            dino_neg = dino_neg.to(device=device).to(dtype=weight_dtype)
-            siglip_neg = siglip_neg.to(device=device).to(dtype=weight_dtype)
-            dino_neg_reshaped = einops.rearrange(dino_neg, "k b c h w -> (k b) c h w")
-            siglip_neg_reshaped = einops.rearrange(siglip_neg, "k b c h w -> (k b) c h w")
-            dino_neg_enc, siglip_neg_enc = self.analogy_encoder(dino_neg_reshaped, siglip_neg_reshaped)
-            negative_prompt_embeds = self.analogy_projector(dino_neg_enc, siglip_neg_enc, batch_size)
-            negative_prompt_embeds = negative_prompt_embeds.repeat(num_images_per_prompt, 1, 1)
-            image_embeddings = th.cat([negative_prompt_embeds, image_embeddings])
-        return image_embeddings
-    @th.no_grad()
-    def __call__(
-        self,
-        analogy_prompt: Union[str, List[str]] = None,
-        num_inference_steps: int = 50,
-        guidance_scale: float = 7.5,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        negative_analogy_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[Union[th.Generator, List[th.Generator]]] = None,
-        latents: Optional[th.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, th.Tensor], None]] = None,
-        callback_steps: int = 1,
-        start_step: int = 0,
-    ):
-        r"""
-        The call function to the pipeline for generation.
-        Args:
-            image (`PIL.Image.Image`, `List[PIL.Image.Image]` or `torch.Tensor`):
-                The image prompt or prompts to guide the image generation.
-            height (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):
-                The width in pixels of the generated image.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                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`.
-            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`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
-                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
-            generator (`torch.Generator`, *optional*):
-                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
-                generation deterministic.
-            latents (`torch.Tensor`, *optional*):
-                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor is generated by sampling using the supplied random `generator`.
-            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.
-            callback (`Callable`, *optional*):
-                A function that calls every `callback_steps` steps during inference. The function is called with the
-                following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function is called. If not specified, the callback is called at
-                every step.
-        Examples:
-        ```py
-        >>> from diffusers import VersatileDiffusionImageVariationPipeline
-        >>> import torch
-        >>> import requests
-        >>> from io import BytesIO
-        >>> from PIL import Image
-        >>> # let's download an initial image
-        >>> url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg"
-        >>> response = requests.get(url)
-        >>> image = Image.open(BytesIO(response.content)).convert("RGB")
-        >>> pipe = VersatileDiffusionImageVariationPipeline.from_pretrained(
-        ...     "shi-labs/versatile-diffusion", torch_dtype=torch.float16
-        ... )
-        >>> pipe = pipe.to("cuda")
-        >>> generator = torch.Generator(device="cuda").manual_seed(0)
-        >>> image = pipe(image, generator=generator).images[0]
-        >>> image.save("./car_variation.png")
-        ```
-        Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] 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.
-        """
-        # 1. Check inputs. Raise error if not correct
-        height = height or self.unet.config.sample_size * self.vae_scale_factor
-        width = width or self.unet.config.sample_size * self.vae_scale_factor
-        # 1. Check inputs. Raise error if not correct
-        self.check_inputs(analogy_prompt, negative_analogy_prompt, height, width, callback_steps)
-        # 2. Define call parameters
-        if isinstance(analogy_prompt, list):
-            batch_size = len(analogy_prompt)
-        elif isinstance(analogy_prompt, tuple):
-            batch_size = 1
-        else:
-            raise ValueError(
-                f"`analogy_prompt` has to be a list of images or a tuple of images but is of type {type(analogy_prompt)}"
-            )
-        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
-        # 3. Encode input prompt
-        analogy_embeddings = self._encode_prompt(
-            analogy_prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_analogy_prompt
-        )
-        # 4. Prepare timesteps
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps = self.scheduler.timesteps
-        # Now this should be from start step onwards
-        timesteps = timesteps[start_step:]
-        # 5. 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,
-            analogy_embeddings.dtype,
-            device,
-            generator,
-            latents,
-        )
-        # 6. Prepare extra step kwargs.
-        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-        # 7. Denoising loop
-        for i, t in enumerate(self.progress_bar(timesteps)):
-            # expand the latents if we are doing classifier free guidance
-            latent_model_input = th.cat([latents] * 2) if do_classifier_free_guidance else latents
-            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-            # predict the noise residual
-            noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=analogy_embeddings).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)
-            # 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 callback is not None and i % callback_steps == 0:
-                step_idx = i // getattr(self.scheduler, "order", 1)
-                callback(step_idx, t, latents)
-        if not output_type == "latent":
-            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
-        else:
-            image = latents
-        image = self.image_processor.postprocess(image, output_type=output_type)
-        if not return_dict:
-            return (image,)
-        return ImagePipelineOutput(images=image)